Dwarf Mongoose Optimization Algorithm

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2022, Номер 391, С. 114570 - 114570

Опубликована: Янв. 28, 2022

Язык: Английский

---

Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems

Mathematical Problems in Engineering, Год журнала: 2021, Номер 2021, С. 1 - 12

Опубликована: Июнь 9, 2021

Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents new metaheuristic named dingo optimizer (DOX) which motivated by the behavior (Canis familiaris dingo). The overall concept to develop this method involving collaborative and social dingoes. developed algorithm based on hunting dingoes that includes exploration, encircling, exploitation. All above prey steps are modeled mathematically implemented in simulator test performance proposed algorithm. Comparative analyses drawn among approach grey wolf (GWO) particle swarm (PSO). Some well-known functions used for comparative study work. results reveal performed significantly better than other nature-inspired algorithms.

Язык: Английский

---

Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization

Advanced Engineering Informatics, Год журнала: 2023, Номер 58, С. 102210 - 102210

Опубликована: Окт. 1, 2023

Язык: Английский

---

An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems

Engineering With Computers, Год журнала: 2021, Номер 38(S4), С. 2797 - 2831

Опубликована: Май 31, 2021

Язык: Английский

---

An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

Biomedical Signal Processing and Control, Год журнала: 2021, Номер 66, С. 102480 - 102480

Опубликована: Фев. 21, 2021

Язык: Английский

---

Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms

Swarm and Evolutionary Computation, Год журнала: 2023, Номер 77, С. 101248 - 101248

Опубликована: Янв. 14, 2023

Metaheuristics are popularly used in various fields, and they have attracted much attention the scientific industrial communities. In recent years, number of new metaheuristic names has been continuously growing. Generally, inventors attribute novelties these algorithms to inspirations from either biology, human behaviors, physics, or other phenomena. addition, algorithms, compared against basic versions metaheuristics using classical benchmark problems, show competitive performances. However, many not rigorously tested on challenging suites with state-of-the-art variants. Therefore, this study, we exhaustively tabulate more than 500 metaheuristics. particular, several representative introduced two aspects, namely, inspirational source essential operators for generating solutions. To comparatively evaluate performance newly proposed metaheuristics, 11 (generally high numbers citations) 4 comprehensively CEC2017 suite. For fair comparisons, a parameter tuning tool named irace is automatically configure parameters all 15 algorithms. whether search bias origin (i.e., center space) investigated. All experimental results analyzed by nonparametric statistical methods, including Bayesian rank-sum test, Friedman Wilcoxon signed-rank critical difference plot test. Moreover, convergence, diversity, trade-off between exploration exploitation also analyzed. The that EBCM algorithm performs similarly same properties such as trade-offs, aspects. 10 less efficient robust likely deteriorate due certain transformations, while affected transformations shifting global optimal point away space. It should be noted that, except EBCM, inferior terms convergence speed ability CEC 2017 functions. rougher present their behavior oscillations) population diversity Finally, important issues relevant research area discussed some potential directions suggested.

Язык: Английский

---

A systematic review of meta-heuristic algorithms in IoT based application

Array, Год журнала: 2022, Номер 14, С. 100164 - 100164

Опубликована: Апрель 9, 2022

Internet-of-Things (IoT) has gained quick popularity with the evolution of technologies such as big data analytics, block-chain, artificial intelligence, machine learning, and deep learning. IoT based systems provides smart automatic framework for efficient decision making automation various task to make human life easy. Meta-heuristic algorithms are self-organized decentralized used solving complex problems using team intelligence. Recently, meta-heuristic been widely a number challenges. This paper presents systematic review unfolding applications. The broad classification existing documented. Further, prominent applications system presented. Moreover, current research questions included illustrate new opportunities researchers. Finally, trends in possible future directions will provide researchers working field system.

Язык: Английский

---

A survey of recently developed metaheuristics and their comparative analysis

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 117, С. 105622 - 105622

Опубликована: Ноя. 25, 2022

The aim of this study was to gather, discuss, and compare recently developed metaheuristics understand the pace development in field make some recommendations for research community practitioners. By thoroughly comprehensively searching literature narrowing search results, we created with a list 57 novel metaheuristic algorithms. Based on availability source code, reviewed analysed optimization capability 26 these algorithms through series experiments. We also evaluated exploitation exploration capabilities by using 50 unimodal functions multimodal functions, respectively. In addition, assessed balance 29 shifted, rotated, composite, hybrid CEC-BC-2017 benchmark functions. Moreover, applicability four real-world constrained engineering problems. To rank algorithms, performed nonparametric statistical test, Friedman mean test. results declared that GBO, PO, MRFO have better capabilities. found MPA, FBI, HBO be most balanced. Finally, based problems, HBO, MA are suitable. Collectively, confidently recommend

Язык: Английский

---

Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design

Materials Testing, Год журнала: 2021, Номер 63(4), С. 336 - 340

Опубликована: Апрель 1, 2021

Abstract Vehicle component design is crucial for developing a vehicle prototype, as optimum parts can lead to cost reduction and performance enhancement of the system. The use metaheuristics optimization has been commonplace due several advantages: robustness simplicity. This paper aims demonstrate shape bracket by using newly invented metaheuristic. new optimizer termed ecogeography-based algorithm (EBO). arguably first application optimizer. problem posed while EBO implemented solve problem. It found that results obtained from are better when compared other optimizers such equilibrium algorithm, marine predators slime mold algorithm.

Язык: Английский

---

A Novel Smell Agent Optimization (SAO): An extensive CEC study and engineering application

Knowledge-Based Systems, Год журнала: 2021, Номер 232, С. 107486 - 107486

Опубликована: Сен. 15, 2021

Язык: Английский

---