Designing angle-independent structural colors using Monte Carlo simulations of multiple scattering.

Disordered nanostructures with correlations on the scale of visible wavelengths can show angle-independent structural colors. These materials could replace dyes in some applications because the color is tunable and resists photobleaching. However, designing nanostructures with a prescribed color is difficult, especially when the application-cosmetics or displays, for example-requires specific component materials. A general approach to solving this constrained design problem is modeling and optimization: Using a model that predicts the color of a given system, one optimizes the model parameters under constraints to achieve a target color. For this approach to work, the model must make accurate predictions, which is challenging because disordered nanostructures have multiple scattering. To address this challenge, we develop a Monte Carlo model that simulates multiple scattering of light in disordered arrangements of spherical particles or voids. The model produces quantitative agreement with measurements when we account for roughness on the surface of the film, particle polydispersity, and wavelength-dependent absorption in the components. Unlike discrete numerical simulations, our model is parameterized in terms of experimental variables, simplifying the connection between simulation and fabrication. To demonstrate this approach, we reproduce the color of the male mountain bluebird (Sialia currucoides) in an experimental system, using prescribed components and a microstructure that is easy to fabricate. Finally, we use the model to find the limits of angle-independent structural colors for a given system. These results enable an engineering design approach to structural color for many different applications.

Engineering design and application of large-scale oil-based drilling cuttings treatment project.

Thermal phase separation technology is a new comprehensive treatment technology, which heats oil-based cuttings to a certain temperature to vaporize oil and water components. Based on a large oil-based drilling cuttings comprehensive utilization project, the engineering design and application effect of thermal phase separation technology were analysed. The practice shows that thermal phase separation technology can reduce the oil content of purified residue to 0.1-0.2%, the average recovery rate of base oil is 94.12% and the annual recovery of base oil is about 4800 t; the purified residue does not have corrosive, leaching toxicity and other dangerous characteristics, and can be used for making bricks or building materials. Thermal phase separation technology is a comprehensive utilization and treatment technology with excellent engineering and environmental benefits, which has a high promotion value.

Immersion Experiences for Biomedical Engineering Undergraduates: Comparing Strategies and Local Partnerships at Two Institutions.

Immersion experiences for undergraduate students in biomedical engineering are key contributors to their ability to identify medical needs. Despite this, as few as 25% of surveyed programs report providing such opportunities. Since 2010 when the National Institute of Health began its R25 grant mechanism to support curricular development toward team-based design, several institutions have established programs for immersion experiences, which provide precedent for their implementation. Published results from such immersion experiences highlight successes in structure and changes in student perspectives after these experiences. As more institutions expand their biomedical engineering curriculum with new immersion-focused programs, it is important to learn from these precedents while also considering opportunities to improve. For newly funded groups that are developing and implementing programs, they may find improved success by strategic use of unique partnerships. However, these partnerships may not be immediately evident to program organizers. Our objective is to discuss two institutions that recently established programs for immersion experience. In the comparison of our two immersion programs, we found five overlapping core features that include: immersion partner collaboration, team-based immersion experiences, needs-finding emphasis, team-based engineering design experiences, and immersion assessment and evaluation. Both programs developed collaborative partnerships with nearby medical schools. Additionally, one program partnered with a community resource (i.e., Human Development Institute). Despite nuanced program differences, we found that students at both programs self-reported increased knowledge or confidence in aspects of the design process (e.g., identifying and refining user needs, concept generation). Our results also highlight student gains unique to their programs - UK students self-reported gains on disability topics and IUPUI students self-reported gains on socioeconomic awareness. In summary, immersion partner collaboration, or partnership, surfaced as a core feature for both programs, and students in both immersion programs endorsed enhanced knowledge or confidence in engineering design.

Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization.

Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmeta-heuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.

Nanomedicine for Acute Brain Injuries: Insight from Decades of Cancer Nanomedicine.

Acute brain injuries such as traumatic brain injury and stroke affect 85 million people a year worldwide, and many survivors suffer from long-term physical, cognitive, or psychosocial impairments. There are few FDA-approved therapies that are effective at preventing, halting, or ameliorating the state of disease in the brain after acute brain injury. To address this unmet need, one potential strategy is to leverage the unique physical and biological properties of nanomaterials. Decades of cancer nanomedicine research can serve as a blueprint for innovation in brain injury nanomedicines, both to emulate the successes and also to avoid potential pitfalls. In this review, we discuss how shared disease physiology between cancer and acute brain injuries can inform the design of novel nanomedicines for acute brain injuries. These disease hallmarks include dysregulated vasculature, an altered microenvironment, and changes in the immune system. We discuss several nanomaterial strategies that can be engineered to exploit these disease hallmarks, for example, passive accumulation, active targeting of disease-associated signals, bioresponsive designs that are "smart", and immune interactions.

Six-Sigma Quality Management of Additive Manufacturing.

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

When Ethics is a Technical Matter: Engineers' Strategic Appeal to Ethical Considerations in Advocating for System Integrity.

Situated in critiques of the "moral muteness" of technical rationality, we examine concepts of ethics and the avoidance of ethical language among Australian gas pipeline engineers. We identify the domains in which they saw ethics as operating, including public safety, environmental protection, sustainability, commercial probity, and modern slavery. Particularly with respect to ethical matters that bear on public safety, in the course of design and operational activities, engineers principally advocated for action using technical language, avoiding reference to potential consequences such as death or destruction of property. Within their organizations, they saw themselves as occupying a technical "line of defense". We argue that this focus on technical language is action-oriented. Ethics tells practitioners of unacceptable outcomes, but it does not guide them in what they need to do to avoid that outcome in practice. We observed some cases where engineers had not made the connection between their role and ethics in the sense of public safety. We argue that muteness on ethical matters can obscure the nature of the risk where technical advice is being taken on by non-technical actors, and where technical actors themselves do not have a clear sense of their public safety obligations.

Spray-Dried Inhalable Powder Formulations of Therapeutic Proteins and Peptides.

Respiratory diseases are among the leading causes of morbidity and mortality worldwide. Innovations in biochemical engineering and understanding of the pathophysiology of respiratory diseases resulted in the development of many therapeutic proteins and peptide drugs with high specificity and potency. Currently, protein and peptide drugs are mostly administered by injections due to their large molecular size, poor oral absorption, and labile physicochemical properties. However, parenteral administration has several limitations such as frequent dosing due to the short half-life of protein and peptide in blood, pain on administration, sterility requirement, and poor patient compliance. Among various noninvasive routes of administrations, the pulmonary route has received a great deal of attention and is a better alternative to deliver protein and peptide drugs for treating respiratory diseases and systemic diseases. Among the various aerosol dosage forms, dry powder inhaler (DPI) systems appear to be promising for inhalation delivery of proteins and peptides due to their improved stability in solid state. This review focuses on the development of DPI formulations of protein and peptide drugs using advanced spray drying. An overview of the challenges in maintaining protein stability during the drying process and stabilizing excipients used in spray drying of proteins and peptide drugs is discussed. Finally, a summary of spray-dried DPI formulations of protein and peptide drugs, their characterization, various DPI devices used to deliver protein and peptide drugs, and current clinical status are discussed.

The Boeing 737 MAX: Lessons for Engineering Ethics.

The crash of two 737 MAX passenger aircraft in late 2018 and early 2019, and subsequent grounding of the entire fleet of 737 MAX jets, turned a global spotlight on Boeing's practices and culture. Explanations for the crashes include: design flaws within the MAX's new flight control software system designed to prevent stalls; internal pressure to keep pace with Boeing's chief competitor, Airbus; Boeing's lack of transparency about the new software; and the lack of adequate monitoring of Boeing by the FAA, especially during the certification of the MAX and following the first crash. While these and other factors have been the subject of numerous government reports and investigative journalism articles, little to date has been written on the ethical significance of the accidents, in particular the ethical responsibilities of the engineers at Boeing and the FAA involved in designing and certifying the MAX. Lessons learned from this case include the need to strengthen the voice of engineers within large organizations. There is also the need for greater involvement of professional engineering societies in ethics-related activities and for broader focus on moral courage in engineering ethics education.

Challenges in interaction modelling with digital human models - A systematic literature review of interaction modelling approaches.

Digital human models (DHM) allow for a proactive ergonomic assessment of products by applying different models describing the user-product interaction. In engineering design, DHM tools are currently not established as computer-aided ergonomics tools, since (among other reasons) the interaction models are either cumbersome to use, unstandardised, time-demanding or not trustworthy. To understand the challenges in interaction modelling, we conducted a systematic literature review with the aim of identification, classification and examination of existing interaction models. A schematic user-product interaction model for DHM is proposed, abstracting existing models and unifying the corresponding terminology. Additionally, nine general approaches to proactive interaction modelling were identified by classifying the reviewed interaction models. The approaches are discussed regarding their scope, limitations, strength and weaknesses. Ultimately, the literature review revealed that prevalent interaction models cannot be considered unconditionally suitable for engineering design since none of them offer a satisfactory combination of genuine proactivity and universal validity. Practitioner summary: This contribution presents a systematic literature review conducted to identify, classify and examine existing proactive interaction modelling approaches for digital human models in engineering design. Ultimately, the literature review revealed that prevalent interaction models cannot be considered unconditionally suitable for engineering design since none of them offer a satisfactory combination of genuine proactivity and universal validity. Abbreviations: DHM: digital human model; CAE: computer-aided engineering; RQ: research question.

An information model to support user-centered design of medical devices.

The process of engineering design requires the product development team to balance the needs and limitations of many stakeholders, including those of the user, regulatory organizations, and the designing institution. This is particularly true in medical device design, where additional consideration must be given for a much more complex user-base that can only be accessed on a limited basis. Given this inherent challenge, few projects exist that consider design domain concepts, such as aspects of a detailed design, a detailed view of various stakeholders and their capabilities, along with the user-needs simultaneously. In this paper, we present a novel information model approach that combines a detailed model of design elements with a model of the design itself, customer requirements, and of the capabilities of the customer themselves. The information model is used to facilitate knowledge capture and automated reasoning across domains with a minimal set of rules by adopting a terminology that treats customer and design specific factors identically, thus enabling straightforward assessments. A uniqueness of this approach is that it systematically provides an integrated perspective on the key usability information that drive design decisions towards more universal or effective outcomes with the very design information impacted by the usability information. This can lead to cost-efficient optimal designs based on a direct inclusion of the needs of customers alongside those of business, marketing, and engineering requirements. Two case studies are presented to show the method's potential as a more effective knowledge management tool with built-in automated inferences that provide design insight, as well as its overall effectiveness as a platform to develop and execute medical device design from a holistic perspective.

Challenges and opportunities for microalgae-mediated CO2 capture and biorefinery.

Aquacultures of microalgae are frontrunners for photosynthetic capture of CO2 from flue gases. Expedient implementation mandates coupling of microalgal CO2 capture with synthesis of fuels and organic products, so as to derive value from biomass. An integrated biorefinery complex houses a biomass growth and harvesting area and a refining zone for conversion to product(s) and separation to desired purity levels. As growth and downstream options require energy and incur loss of carbon, put together, the loop must be energy positive, carbon negative, or add substantial value. Feasibility studies can, thus, aid the choice from among the rapidly evolving technological options, many of which are still in the early phases of development. We summarize basic engineering calculations for the key steps of a biorefining loop where flue gases from a thermal power station are captured using microalgal biomass along with subsequent options for conversion to fuel or value added products. An assimilation of findings from recent laboratory and pilot-scale experiments and life cycle analysis (LCA) studies is presented as carbon and energy yields for growth and harvesting of microalgal biomass and downstream options. Of the biorefining options, conversion to the widely studied biofuel, ethanol, and manufacture of the platform chemical, succinic acid are presented. Both processes yield specific products and do not demand high-energy input but entail 60-70% carbon loss through fermentative respiration. Thermochemical conversions, on the other hand, have smaller carbon and energy losses but yield a mixture of products.

A concept ideation framework for medical device design.

Medical device design is a challenging process, often requiring collaboration between medical and engineering domain experts. This collaboration can be best institutionalized through systematic knowledge transfer between the two domains coupled with effective knowledge management throughout the design innovation process. Toward this goal, we present the development of a semantic framework for medical device design that unifies a large medical ontology with detailed engineering functional models along with the repository of design innovation information contained in the US Patent Database. As part of our development, existing medical, engineering, and patent document ontologies were modified and interlinked to create a comprehensive medical device innovation and design tool with appropriate properties and semantic relations to facilitate knowledge capture, enrich existing knowledge, and enable effective knowledge reuse for different scenarios. The result is a Concept Ideation Framework for Medical Device Design (CIFMeDD). Key features of the resulting framework include function-based searching and automated inter-domain reasoning to uniquely enable identification of functionally similar procedures, tools, and inventions from multiple domains based on simple semantic searches. The significance and usefulness of the resulting framework for aiding in conceptual design and innovation in the medical realm are explored via two case studies examining medical device design problems.

Inhalable nanoparticulate powders for respiratory delivery.

Nanoparticles are extensively studied for drug delivery and are proving to be effective in drug delivery and the diagnostic field. Drug delivery to lungs has its advantages over other routes of administration. Inhalable powders consisting of nanoparticles are gaining much interest in respiratory research and clinical therapy. Particle engineering technique is a key factor to develop inhalable formulations that can successfully deliver drug with improved therapeutic effect and enhanced targeting. Inhalable nanoparticles in the solid-state dry powders for targeted pulmonary delivery offer unique advantages and are an exciting new area of research. Nasal delivery of inhalable nanoparticulate powders is gaining research attention recently, particularly in vaccine applications, systemic drug delivery in the treatment of pain, and non-invasive brain targeting. Fundamental aspects and recent advancements along with future prospects of inhalable powders consisting of nanoparticles in the solid-state for respiratory delivery are presented. FROM THE CLINICAL EDITOR: The advance in nanotechnology has enabled the design of new drug delivery systems through inhalation, which has many advantages over traditional delivery systems. This comprehensive review describes and discusses the current status, drug design and modification for targeted delivery and challenges of the use of nanoparticles in the respiratory tract.

Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift.

Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.

The TrollLabs open hackathon dataset: Generative AI and large language models for prototyping in engineering design.

The TrollLabs Open dataset includes comprehensive information that offers a comparison of design practices and outcomes between human participants and Generative AI during a hackathon event. The dataset was curated through the running of a prototyping hackathon designed to assess the abilities and performance of generative AI, specifically ChatGPT, in the early stages of engineering design. This assessment involved comparing ChatGPT's performance to that of experienced engineering students in a hackathon setting, where participants competed by making a prototype that fires a NERF dart as far as possible. In this setup, all ideas, concepts, strategies, and actions undertaken by the AI-controlled team were autonomously generated by the ChatGPT, without human intervention or guidance, but implemented by two participants. Five self-directed baseline teams competed against the AI team. The dataset comprises 116 prototype entries and 433 edges (connection) that enable comparative analysis of design practices and performance between the team instructed solely by generative AI and baseline teams of experienced engineering design students. Prototypes and their attribute data were captured using Pro2booth, an online prototype capture platform running on participants' phones and computers. The dataset includes a transcript of the conversation between ChatGPT and the team responsible for implementing its recommendations, featuring 97 exchanges of prompts and responses. It contains the initial prompt used to instruct the AI, the objective and rules of the hackathon and the objective performance of teams, showing the ChatGPT team finishing 2nd among six teams. To the authors' knowledge, the TrollLabs Open dataset is the first and only open resource that directly compares the performance of generative AI with human teams in an engineering design context. Thus, it is intended to be a valuable resource to design researchers, engineering and design students, educators, and industry professionals seeking to find strategies for implementing generative AI tools in their design processes. By offering a comprehensive data collection, the dataset enables external researchers to conduct in-depth analyses that could highlight the practical implications of integrating generative AI in design practices, possibly providing an overview of its limitations and presenting recommendations for improved integration in the design process.

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems.

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems.

There are a lot of multi-objective optimization problems (MOPs) in the real world, and many multi-objective evolutionary algorithms (MOEAs) have been presented to solve MOPs. However, obtaining non-dominated solutions that trade off convergence and diversity remains a major challenge for a MOEA. To solve this problem, this paper designs an efficient multi-objective sine cosine algorithm based on a competitive mechanism (CMOSCA). In the CMOSCA, the ranking relies on non-dominated sorting, and the crowding distance rank is utilized to choose the outstanding agents, which are employed to guide the evolution of the SCA. Furthermore, a competitive mechanism stemming from the shift-based density estimation approach is adopted to devise a new position updating operator for creating offspring agents. In each competition, two agents are randomly selected from the outstanding agents, and the winner of the competition is integrated into the position update scheme of the SCA. The performance of our proposed CMOSCA was first verified on three benchmark suites (i.e., DTLZ, WFG, and ZDT) with diversity characteristics and compared with several MOEAs. The experimental results indicated that the CMOSCA can obtain a Pareto-optimal front with better convergence and diversity. Finally, the CMOSCA was applied to deal with several engineering design problems taken from the literature, and the statistical results demonstrated that the CMOSCA is an efficient and effective approach for engineering design problems.

An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications.

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency.

An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems.

As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.