A multi-factor combined traffic flow prediction model with secondary decomposition and improved entropy weight method

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124424 - 124424

Опубликована: Июнь 18, 2024

Язык: Английский

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Dream Optimization Algorithm (DOA): A novel metaheuristic optimization algorithm inspired by human dreams and its applications to real-world engineering problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2025, Номер 436, С. 117718 - 117718

Опубликована: Янв. 9, 2025

Язык: Английский

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Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Год журнала: 2023, Номер 8(6), С. 507 - 507

Опубликована: Окт. 23, 2023

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates natural behavior of lyrebirds in wild is introduced. The fundamental inspiration LOA strategy when faced with danger. situation, scan their surroundings carefully, then either run away or hide somewhere, immobile. theory described and mathematically modeled two phases: (i) exploration based on simulation lyrebird escape (ii) exploitation hiding strategy. performance was evaluated optimization CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. results show proposed approach has high ability terms exploration, exploitation, balancing them during search process problem-solving space. order evaluate capability dealing tasks, obtained from were compared twelve well-known algorithms. superior competitor algorithms by providing better most benchmark functions, achieving rank first best optimizer. A statistical analysis shows significant superiority comparison addition, efficiency handling real-world applications investigated through twenty-two constrained problems 2011 four engineering design problems. effective tasks while

Язык: Английский

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Chaotic Sand Cat Swarm Optimization

Mathematics, Год журнала: 2023, Номер 11(10), С. 2340 - 2340

Опубликована: Май 17, 2023

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This combines the features of recently introduced SCSO with concept chaos. The basic aim to integrate chaos feature non-recurring locations into SCSO’s core search process improve global performance convergence behavior. Thus, randomness in can be replaced by chaotic map due similar better statistical dynamic properties. addition these advantages, low consistency, local optimum trap, inefficiency search, population diversity issues are also provided. CSCSO, several maps implemented more efficient behavior exploration exploitation phases. Experiments conducted on wide variety well-known test functions increase reliability results, as well real-world was applied total 39 multidisciplinary It found 76.3% responses compared best-developed variant other chaotic-based metaheuristics tested. extensive experiment indicates that CSCSO excels providing acceptable results.

Язык: Английский

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Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Expert Systems with Applications, Год журнала: 2023, Номер 227, С. 120367 - 120367

Опубликована: Май 6, 2023

Язык: Английский

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