An enhanced dung beetle optimizer with multiple strategies for robot path planning

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 7, 2025

In order to make up for the shortcomings of original dung beetle optimization algorithm, such as low population diversity, insufficient global exploration ability, being easy fall into local and unsatisfactory convergence accuracy, etc. An improved algorithm using hybrid multi- strategy is proposed. Firstly, cubic chaotic mapping approach used initialize improve expand search range solution space, enhance ability. Secondly, cooperative utilized strength communication between individual beetles groups in foraging stage space Thirdly, T-distribution mutation differential evolutionary variation strategies are introduced provide perturbation diversity avoid falling optimization. Fourthly, proposed algorithm(named SSTDBO) compared with other algorithms, including GODBO, QHDBO, DBO, KOA, NOA, WOA HHO, by 29 benchmark test functions CEC2017. The results show that has stronger robustness algorithm's performance substantially enhanced. Finally, applied solve real-world robot path planning engineering cases, demonstrate its effectiveness dealing real which further verified how noteworthy enhanced strategy's efficacy superiority addressing cases.

Language: Английский

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An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 3, 2024

Abstract Aiming at the problems of insufficient ability artificial COA in late optimization search period, loss population diversity, easy to fall into local extreme value, resulting slow convergence and lack exploration ability; In this paper, an improved algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy alert updating is proposed enhance performance (shorted as TNTWCOA). The introduces sequence mechanism initialize position. position distribution initial solution more uniform, high quality generated, richness increased, problem poor uneven Coati Optimization Algorithm solved. phase, inertial weight factor introduced coordinate global algorithm. exploitation increase diversity individual under low fitness value improve jump out optimal value. At same time, update algorithm, so that it can within optional range. When aware danger, edge will quickly move safe area obtain a better position, while middle randomly get closer other Coatis. IEEE CEC2017 with 29 classic test functions were used evaluate speed, accuracy indicators TNTWCOA Meanwhile, was verify 4 engineering design problems, such pressure vessel welding beam design. results are compared Improved (ICOA), (COA), Golden Jackal (GJO), Osprey (OOA), Sand Cat Swarm (SCSO), Subtraction-Average-Based Optimizer (SABO). experimental show significantly improves speed accuracy, has good robustness. Three‑bar truss problem, Gear Train Design Problem, Speed reducer shows strong advantage. superior practicability verified.

Language: Английский

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A hybrid slime mould algorithm with Levy Flight based mutation for malaria parasite detection

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(6)

Published: April 16, 2025

Language: Английский

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A Multi-Strategy Adaptive Coati Optimization Algorithm for Constrained Optimization Engineering Design Problems

Biomimetics, Journal Year: 2025, Volume and Issue: 10(5), P. 323 - 323

Published: May 16, 2025

Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati algorithm (COA) is novel meta-heuristic known its robust search capabilities rapid convergence rate. However, effectiveness COA compromised by homogeneity initial population reliance on random strategies prey hunting. To address these issues, multi-strategy adaptive (MACOA) presented this paper. Firstly, Lévy flights incorporated into initialization phase to produce high-quality solutions. Subsequently, nonlinear inertia weight factor integrated exploration bolster algorithm’s global accelerate convergence. Finally, vigilante mechanism introduced exploitation improve capacity escape local optima. Comparative experiments with many existing conducted using CEC2017 test functions, proposed applied seven representative design problems. MACOA’s average rankings three dimensions (30, 50, 100) were 2.172, 1.897, 1.759, respectively. results show improved speed better performance.

Language: Английский

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