Expert Systems with Applications, Год журнала: 2023, Номер 236, С. 121202 - 121202
Опубликована: Авг. 26, 2023
Язык: Английский
IEEE Access, Год журнала: 2022, Номер 10, С. 121615 - 121640
Опубликована: Янв. 1, 2022
Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem.Most of these draw inspiration from collective intelligence hunting behavior animals in nature.This paper proposes a novel metaheuristic algorithm called Giant Trevally Optimizer (GTO).In nature, giant trevally feeds many animals, including fish, cephalopods, seabirds (sooty terns).In this work, unique strategies when mathematically modeled divided into three main steps.In first step, foraging movement patterns trevallies simulated.In second choose appropriate area terms food where they can hunt prey.In last starts chase seabird (prey).When prey is close enough trevally, jumps out water attacks air or even snatches surface.The performance GTO compared against state-of-the-art metaheuristics set forty benchmark functions with different characteristics five complex problems.The comparative study, scalability analysis, statistical analysis based Wilcoxon rank sum test, findings suggest that proposed an efficient optimizer optimization.Note MATLAB source codes will be publicly available after acceptance paper.
Язык: Английский
Процитировано
93
Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 419, С. 116582 - 116582
Опубликована: Ноя. 21, 2023
Язык: Английский
Процитировано
68
Knowledge-Based Systems, Год журнала: 2024, Номер 295, С. 111737 - 111737
Опубликована: Апрель 12, 2024
This study proposes a novel artificial protozoa optimizer (APO) that is inspired by in nature. The APO mimics the survival mechanisms of simulating their foraging, dormancy, and reproductive behaviors. was mathematically modeled implemented to perform optimization processes metaheuristic algorithms. performance verified via experimental simulations compared with 32 state-of-the-art Wilcoxon signed-rank test performed for pairwise comparisons proposed algorithms, Friedman used multiple comparisons. First, tested using 12 functions 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, solve five popular engineering design problems continuous space constraints. Moreover, applied multilevel image segmentation task discrete experiments confirmed could provide highly competitive results problems. source codes Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.
Язык: Английский
Процитировано
39
PLoS ONE, Год журнала: 2024, Номер 19(8), С. e0308474 - e0308474
Опубликована: Авг. 19, 2024
This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The (HO) is novel meta-heuristic methodology draws inspiration from natural behaviour of hippos. HO built upon trinary-phase model incorporates mathematical representations crucial aspects Hippo's behaviour, including their movements aquatic environments, defense mechanisms against predators, and avoidance strategies. conceptual framework forms basis for developing multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions size constraints concerning stresses on individual sections constituent parts, these problems also involved competing objectives, such as reducing weight structure maximum nodal displacement. findings six popular methods were used compare results. Four industry-standard performance measures this comparison qualitative examination finest Pareto-front plots generated by each algorithm. average values obtained Friedman rank test analysis unequivocally showed MOHO outperformed other resolving significant quickly. In addition finding preserving more Pareto-optimal sets, recommended algorithm produced excellent convergence variance objective decision fields. demonstrated its potential navigating objectives through diversity analysis. Additionally, swarm effectively visualize MOHO's solution distribution across iterations, highlighting superior behaviour. Consequently, exhibits promise valuable method issues.
Язык: Английский
Процитировано
35
Neural Computing and Applications, Год журнала: 2024, Номер 36(12), С. 6721 - 6740
Опубликована: Фев. 12, 2024
Язык: Английский
Процитировано
33
Energy Reports, Год журнала: 2024, Номер 11, С. 5436 - 5455
Опубликована: Май 21, 2024
The energy management (EM) solution of the microgrids (MGs) is a crucial task to attain most economic, reliable and sustainable operation state MGs. This paper aims solve optimal scheduling stochastic EM problem smart MG without with demand side response (DSR) including MT, FC, PV, WT, battery storage system (BSS). A study case in Wenzhou city China conducted reduce cost maximize utilization renewable energy. uncertainties like loading, temperature, solar irradiance wind speed are considered which were obtained from real meteorological data. normal, lognormal, Weibull PDFs as well Monte-Carlo RBS methods used for uncertainty modelling. modified artificial rabbit optimization (MARO) proposed based on three strategies fitness-distance balance, exploitation mechanism PDO quasi-opposite-based learning (QOBL) boost exploration phases traditional ARO. statistical non-parametric tests applied via benchmark functions validate performance MARO. As per results, MARO superior compared other techniques reduced 252.0721€ct/day 184.8435€ct/day saving 26.86 % considerably application DSR.
Язык: Английский
Процитировано
16
Evolutionary Intelligence, Год журнала: 2025, Номер 18(1)
Опубликована: Янв. 9, 2025
Язык: Английский
Процитировано
3
Engineering Science and Technology an International Journal, Год журнала: 2023, Номер 41, С. 101408 - 101408
Опубликована: Апрель 10, 2023
Metaheuristic optimization algorithms are global approaches that manage the search process to efficiently explore spaces associated with different problems. Seahorse (SHO) is a novel swarm-based metaheuristic method inspired by certain behaviors of sea horses. The SHO algorithm mimics movement, hunting, and breeding behavior horses in nature. Chaotic maps effectively used improve performance avoiding local optimum increasing speed convergence. In this study, 10 chaotic have been employed for first time produce values rather than random SHO, method. purpose using generate their increase convergence original avoid optimum. 33 benchmark functions, consisting unimodal, multimodal, fixed-dimension CEC2019, utilized assess (CSHO), which introduced study. addition, proposed CSHO has compared four literature, namely Sine Cosine Algorithm, Salp Swarm Whale Optimization Particle Optimization. Statistical analyses obtained results also performed. then implemented 4 real-world engineering design problems, including welded beam, pressure vessel, tension/compression spring, reducer. popular methods literature. Experimental show it gives successful promising algorithm.
Язык: Английский
Процитировано
29
Advances in Engineering Software, Год журнала: 2023, Номер 184, С. 103517 - 103517
Опубликована: Июнь 28, 2023
Язык: Английский
Процитировано
28
Energy Conversion and Management, Год журнала: 2023, Номер 299, С. 117809 - 117809
Опубликована: Ноя. 3, 2023
Язык: Английский
Процитировано
28