Hyperplane-Assisted Multi-objective Particle Swarm Optimization with Twofold Proportional Assignment Strategy DOI Creative Commons
Qian Song, Yanmin Liu, Xiaoyan Zhang

et al.

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: Dec. 18, 2024

In the simultaneous optimization of multiple objectives, how to balance convergence promotion and diversity preservation in evolutionary process is a key challenging problem. this research, hyperplane-assisted multi-objective particle swarm with twofold proportional assignment strategy (tpahaMOPSO) suggested ameliorate performance MOPSO. First, external archive maintained combination hyperplane-based evaluation shift-based density estimation retain high-quality candidate solutions. Second, scheme designed search surrounding region solutions better potential emphasize diversity, respectively. Third, domination relationship difference are combined select more reasonable individual historical best reduce risk aggregation. Finally, proposed tpahaMOPSO was compared ten representative advanced algorithms on 22 widely used test functions different characteristics. The simulation results present that developed got result 11 benchmark for both IGD HV criteria. Concurrently, Friedman applied ranking analysis algorithm also obtained excellent statistical results. promising strong competitiveness have been verified by experimental studies.

Language: Английский

Multi-strategy enhanced marine predator algorithm: performance investigation and application in intrusion detection DOI Creative Commons
Zhongmin Wang, Yujun Zhang, Jun Yu

et al.

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 19, 2025

Language: Английский

Citations

0
Predicting the chemical equilibrium point of reacting components in gaseous mixtures through a novel Hierarchical Manta-Ray Foraging Optimization Algorithm DOI Creative Commons
Oğuz Emrah Turgut, Hadi Genceli, Mustafa Asker

et al.

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

Published: April 1, 2025

Abstract This study proposes a Hierarchical Manta-Ray Foraging Optimization (HMRFO) algorithm for calculating the equilibrium points of chemical reactions. To improve solution diversity in trial population and enhance general optimization effectivity algorithm, an ordered hierarchy is integrated into original taking account efficient search strategies Elite-Opposition learning, Dynamic Opposition Learning, Quantum operator. Within this proposed concept, Manta-ray divided three main sub-populations: Elite Oppositional learning scheme manipulates top elite individuals, equations update average members, quantum-based process worst members. The improved MRFO applied to hundred 30D 500D benchmark functions, results have been compared those obtained from state-of-art metaheuristic optimizers. Then, optimizer solved twenty-eight test problems previously employed CEC-2013 competitions, corresponding were benchmarked against well-reputed metaheuristics. research also suggests novel mathematical model solving ideal gas mixtures. Four challenging case studies related performed by HMRFO varying conditions, it observed that can effectively cope with tedious nonlinearities complexities governing thermodynamic models associated gaseous reacting mixture components.

Language: Английский

Citations

0
Integrating Competitive Framework into Differential Evolution: Comprehensive performance analysis and application in brain tumor detection DOI
Rui Zhong, Zhongmin Wang, Yu-Jun Zhang

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112995 - 112995

Published: March 1, 2025

Language: Английский

Citations

0
Mixed-Strategy Harris Hawk Optimization Algorithm for UAV Path Planning and Engineering Applications DOI Creative Commons

Guoping You,

Yudan Hu, Chao Lian

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10581 - 10581

Published: Nov. 16, 2024

This paper introduces the mixed-strategy Harris hawk optimization (MSHHO) algorithm as an enhancement to address limitations of conventional (HHO) in solving complex problems. HHO often faces challenges such susceptibility local optima, slow convergence, and inadequate precision global solution-seeking. MSHHO integrates four innovative strategies bolster HHO’s effectiveness both exploitation exploration. These include a positive charge repulsion strategy for diverse population initialization, nonlinear decreasing parameter heighten competitiveness, introduction Gaussian random walk, mutual benefit-based position updates enhance mobility escape optima. Empirical validation on 12 benchmark functions from CEC2005 comparison with 10 established algorithms affirm MSHHO’s superior performance. Applications three real-world engineering problems UAV flight trajectory further demonstrate efficacy overcoming challenges. study underscores robust framework enhanced exploration capabilities, significantly improving convergence accuracy speed applications.

Language: Английский

Citations

1
MSBES: an improved bald eagle search algorithm with multi- strategy fusion for engineering design and water management problems DOI
Wenchuan Wang,

Wei-can Tian,

Kwok‐wing Chau

et al.

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 81(1)

Published: Dec. 6, 2024

Language: Английский

Citations

0
A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems DOI Creative Commons

Wuke Li,

Xiong Yang,

Yuchen Yin

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 10(1), P. 14 - 14

Published: Dec. 31, 2024

The RIME algorithm is a novel physical-based meta-heuristic with strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration exploitation behaviors by constructing rime-ice growth process. However, comes couple of disadvantages: limited exploratory capability, slow convergence, inherent asymmetry between exploitation. An improved version more efficiency adaptability these issues now the form Hybrid Estimation Rime-ice Optimization, short, HERIME. A probabilistic model-based sampling approach estimated distribution utilized enhance quality population boost its capability. roulette-based fitness distance balanced selection strategy used strengthen hard-rime phase effectively balance phases We validate HERIME using 41 functions from IEEE CEC2017 CEC2022 test suites compare accuracy, stability four classical recent metaheuristic algorithms as well five advanced reveal fact that proposed outperforms all them. Statistical research Friedman Wilcoxon rank sum also confirms excellent performance. Moreover, ablation experiments effectiveness each individually. Thus, experimental results show has better search accuracy effective dealing problems.

Language: Английский

Citations

0
Hyperplane-Assisted Multi-objective Particle Swarm Optimization with Twofold Proportional Assignment Strategy DOI Creative Commons
Qian Song, Yanmin Liu, Xiaoyan Zhang

et al.

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: Dec. 18, 2024

In the simultaneous optimization of multiple objectives, how to balance convergence promotion and diversity preservation in evolutionary process is a key challenging problem. this research, hyperplane-assisted multi-objective particle swarm with twofold proportional assignment strategy (tpahaMOPSO) suggested ameliorate performance MOPSO. First, external archive maintained combination hyperplane-based evaluation shift-based density estimation retain high-quality candidate solutions. Second, scheme designed search surrounding region solutions better potential emphasize diversity, respectively. Third, domination relationship difference are combined select more reasonable individual historical best reduce risk aggregation. Finally, proposed tpahaMOPSO was compared ten representative advanced algorithms on 22 widely used test functions different characteristics. The simulation results present that developed got result 11 benchmark for both IGD HV criteria. Concurrently, Friedman applied ranking analysis algorithm also obtained excellent statistical results. promising strong competitiveness have been verified by experimental studies.

Language: Английский

Citations

0