Dynamic Chaotic Opposition-Based Learning-Driven Hybrid Aquila Optimizer and Artificial Rabbits Optimization Algorithm: Framework and Applications DOI Open Access
Yangwei Wang, Yaning Xiao, Guo Yan-ling

et al.

Processes, Journal Year: 2022, Volume and Issue: 10(12), P. 2703 - 2703

Published: Dec. 14, 2022

Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy premature convergence when addressing some complex cases due to the insufficient exploitation phase. In contrast, ARO possesses very competitive potential, but its ability needs be more satisfactory. To ameliorate above-mentioned limitations in a single algorithm achieve better overall performance, this paper proposes novel chaotic opposition-based learning-driven hybrid called CHAOARO. Firstly, global phase of is combined with local maintain respective valuable search capabilities. Then, an adaptive switching mechanism (ASM) designed balance procedures. Finally, we introduce learning (COBL) strategy avoid fall into optima. comprehensively verify effectiveness superiority proposed work, CHAOARO compared original AO, ARO, several state-of-the-art algorithms on 23 classical benchmark functions IEEE CEC2019 test suite. Systematic comparisons demonstrate that can significantly outperform other competitor methods terms accuracy, speed, robustness. Furthermore, promising prospect real-world applications highlighted by resolving five industrial engineering design problems photovoltaic (PV) model parameter identification problem.

Language: Английский

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems DOI Creative Commons
Jui‐Sheng Chou,

Asmare Molla

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Nov. 10, 2022

Abstract The complexity of engineering optimization problems is increasing. Classical gradient-based algorithms are a mathematical means solving complex whose ability to do so limited. Metaheuristics have become more popular than exact methods for because their simplicity and the robustness results that they yield. Recently, population-based bio-inspired been demonstrated perform favorably in wide range problems. jellyfish search optimizer (JSO) one such metaheuristic algorithm, which based on food-finding behavior ocean. According literature, JSO outperforms many well-known meta-heuristics benchmark functions real-world applications. can also be used conjunction with other artificial intelligence-related techniques. success diverse motivates present comprehensive discussion latest findings related JSO. This paper reviews various issues associated JSO, as its inspiration, variants, applications, will provide developments research concerning systematic review contributes development modified versions hybridization improve upon original help researchers develop superior recommendations add-on intelligent agents.

Language: Английский

Citations

46
IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems DOI Creative Commons
Yaning Xiao, Yanling Guo, Hao Cui

et al.

Mathematical Biosciences & Engineering, Journal Year: 2022, Volume and Issue: 19(11), P. 10963 - 11017

Published: Jan. 1, 2022

<abstract><p>Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising capability but insufficient mechanisms. Based on characteristics both algorithms, this paper, we propose an improved hybrid optimizer called IHAOAVOA to overcome deficiencies single algorithm provide higher-quality solutions for solving optimization problems. First, combined retain valuable search competence each. Then, a new composite opposition-based learning (COBL) designed increase population diversity help escape from optima. In addition, more effectively guide process balance exploitation, fitness-distance (FDB) selection strategy introduced modify core position update formula. The performance proposed comprehensively investigated analyzed by comparing against basic AO, AVOA, six state-of-the-art 23 classical benchmark functions IEEE CEC2019 test suite. Experimental results demonstrate achieves superior solution accuracy, convergence speed, optima avoidance than comparison methods most functions. Furthermore, practicality highlighted five engineering design Our findings reveal technique also highly competitive when addressing real-world tasks. source code publicly available at <a href="https://doi.org/10.24433/CO.2373662.v1" target="_blank">https://doi.org/10.24433/CO.2373662.v1</a>.</p></abstract>

Language: Английский

Citations

41
DTCSMO: An efficient hybrid starling murmuration optimizer for engineering applications DOI
Gang Hu, Jingyu Zhong, Guo Wei

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2023, Volume and Issue: 405, P. 115878 - 115878

Published: Jan. 10, 2023

Language: Английский

Citations

39
EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications DOI Creative Commons
Gang Hu, Jiao Wang, Min Li

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(4), P. 851 - 851

Published: Feb. 7, 2023

The jellyfish search (JS) algorithm impersonates the foraging behavior of in ocean. It is a newly developed metaheuristic that solves complex and real-world optimization problems. global exploration capability robustness JS are strong, but still has significant development space for solving problems with high dimensions multiple local optima. Therefore, this study, an enhanced (EJS) developed, three improvements made: (i) By adding sine cosine learning factors strategy, can learn from both random individuals best individual during Type B motion swarm to enhance accelerate convergence speed. (ii) escape operator, skip trap optimization, thereby, exploitation ability algorithm. (iii) applying opposition-based quasi-opposition population distribution increased, strengthened, more diversified, better selected present new opposition solution participate next iteration, which solution’s quality, meanwhile, speed faster algorithm’s precision increased. In addition, performance EJS was compared those incomplete improved algorithms, some previously outstanding advanced methods were evaluated on CEC2019 test set as well six examples real engineering cases. results demonstrate increase calculation practical applications also verify its superiority effectiveness constrained unconstrained problems, therefore, suggests future possible such

Language: Английский

Citations

37
A reinforcement learning-based hybrid Aquila Optimizer and improved Arithmetic Optimization Algorithm for global optimization DOI
Haiyang Liu, Xingong Zhang, Hanxiao Zhang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 224, P. 119898 - 119898

Published: March 21, 2023

Language: Английский

Citations

34
A Comprehensive Survey on Arithmetic Optimization Algorithm DOI Open Access
Krishna Gopal Dhal, Buddhadev Sasmal, Arunita Das

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(5), P. 3379 - 3404

Published: March 15, 2023

Language: Английский

Citations

31
An Inclusive Survey on Marine Predators Algorithm: Variants and Applications DOI Open Access
Rebika Rai, Krishna Gopal Dhal, Arunita Das

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(5), P. 3133 - 3172

Published: Feb. 24, 2023

Language: Английский

Citations

29
Application of SVR models built with AOA and Chaos mapping for predicting tunnel crown displacement induced by blasting excavation DOI
Chuanqi Li, Xiancheng Mei

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 147, P. 110808 - 110808

Published: Sept. 4, 2023

Language: Английский

Citations

24
Enhancing grasshopper optimization algorithm (GOA) with levy flight for engineering applications DOI Creative Commons
Lei Wu, Jiawei Wu,

Tengbin Wang

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Jan. 4, 2023

The grasshopper optimization algorithm (GOA) is a meta-heuristic proposed in 2017 mimics the biological behavior of swarms seeking food sources nature for solving problems. Nonetheless, some shortcomings exist origin GOA, and GOA global search ability more or less insufficient precision also needs to be further improved. Although there are many different variants literature, problem inefficient rough has still emerged variants. Aiming at these deficiencies, this paper develops an improved version with Levy Flight mechanism called LFGOA alleviate GOA. achieved suitable balance between exploitation exploration during searching most promising region. performance tested using 23 mathematical benchmark functions comparison eight well-known algorithms seven real-world engineering statistical analysis experimental results show efficiency LFGOA. According obtained results, it possible say that can potential alternative solution problems as high capabilities.

Language: Английский

Citations

23
An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy DOI Creative Commons
Xing Wang, Qian Liu, Li Zhang

et al.

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 191 - 191

Published: May 4, 2023

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic derived from the distant sense of hearing sand cats, which shows excellent performance in some large-scale problems. However, SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, tendency to be trapped topical optimum. To escape these demerits, an adaptive based on Cauchy mutation optimal neighborhood disturbance strategy (COSCSO) are provided this study. First foremost, introduction nonlinear parameter favor scaling up global search helps retrieve optimum colossal space, preventing it being caught Secondly, operator perturbs step, accelerating speed improving efficiency. Finally, diversifies population, broadens enhances exploitation. reveal COSCSO, was compared with alternative algorithms CEC2017 CEC2020 competition suites. Furthermore, COSCSO is further deployed solve six engineering The experimental results that strongly competitive capable practical

Language: Английский

Citations

23