Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems

Luo, Qifang; Yin, Shihong; Zhou, Guo; Meng, Weiping; Zhao, Yixin; Zhou, Yongquan

doi:10.1007/s00158-023-03568-y

Cited by 22 publications

(6 citation statements)

References 72 publications

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“…Kaveh A et al [72] adopted an elitist strategy in the replacement phase of an SMA to increase the convergence rate of the proposed ISMA. Luo Qifang et al [73] proposed an MOEOSMA by adding an elite archiving mechanism to promote the overall convergence speed.…”

Section: Elite Strategymentioning

confidence: 99%

“…The operator fusion replaces or modifies the SMA operators with other algorithms or combines different operators from both algorithms to solve the problems of the original SMA. The operator fusion approach is mostly apparent in the hybrid version of SMA combined with the EO, EO, and GA. As for the hybridization of SMA and EQ in [58,73,76,80], the operators observed that the best SMA was replaced by the first solution in the equilibrium pool of EO. Additionally, the mutation and crossover operators of EO and GA are usually adopted to update the position during the search for exiting the local optimum position.…”

Section: Hybridized Version Of the Smamentioning

confidence: 99%

“…Yuanfei Wei et al [80] changed the random search operator in an SMA with an EO strategy to enhance the population diversity. Luo Qifang et al [73] integrated the equilibrium pool's strategy to the proposed EOSMA at the stage of population initialization, helping the proposed algorithm to improve the exploration outcomes.…”

Section: Hybridization With the Equilibrium Optimizer (Eo)mentioning

confidence: 99%

“…Al-Kaabi M et al [78] presented an MOSMA for attacking multi-objective optimal power-flow problems. Luo Qifang et al [73] proposed an MOEOSMA and assessed it for engineering problems, which indicated its overall efficiency. Peng C et al [107] proposed an MOSMA and optimized SVR parameters by MOSMA for global convergence.…”

Section: Multi-objective Version Of An Smamentioning

confidence: 99%

“…Kaveh A et al [72] used the proposed ISMA for three largescale benchmark dome trusses. Qifang Luo et al [73] used the proposed MOEOSMA to solve eight real-world multi-objective constraint engineering problems. Qifang Luo et al [73] used the proposed MOEOSMA to solve large-scale truss structure optimization problems.…”

Section: Engineering Designmentioning

confidence: 99%

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Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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Section: Elite Strategymentioning

confidence: 99%

Section: Hybridized Version Of the Smamentioning

confidence: 99%

Section: Hybridization With the Equilibrium Optimizer (Eo)mentioning

confidence: 99%

Section: Multi-objective Version Of An Smamentioning

confidence: 99%

Section: Engineering Designmentioning

confidence: 99%

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Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

Self Cite

View full text Add to dashboard Cite

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Equilibrium optimizer-based harmony search algorithm with nonlinear dynamic domains and its application to real-world optimization problems

Wang,

Ouyang,

et al. 2024

Artif Intell Rev

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Harmony Search (HS) algorithm is a swarm intelligence algorithm inspired by musical improvisation. Although HS has been applied to various engineering problems, it faces challenges such as getting trapped in local optima, slow convergence speed, and low optimization accuracy when applied to complex problems. To address these issues, this paper proposes an improved version of HS called Equilibrium Optimization-based Harmony Search Algorithm with Nonlinear Dynamic Domains (EO-HS-NDD). EO-HS-NDD integrates multiple leadership-guided strategies from the Equilibrium Optimizer (EO) algorithm, using harmony memory considering disharmony and historical harmony memory, while leveraging the hidden guidance direction information from the Equilibrium Optimizer. Additionally, the algorithm designs a nonlinear dynamic convergence domain to adaptively adjust the search space size and accelerate convergence speed. Furthermore, to balance exploration and exploitation capabilities, appropriate adaptive adjustments are made to Harmony Memory Considering Rate (HMCR) and Pitch Adjustment Rate (PAR). Experimental validation on the CEC2017 test function set demonstrates that EO-HS-NDD outperforms HS and nine other HS variants in terms of robustness, convergence speed, and optimization accuracy. Comparisons with advanced versions of the Differential Evolution (DE) algorithm also indicate that EO-HS-NDD exhibits superior solving capabilities. Moreover, EO-HS-NDD is applied to solve 15 real-world optimization problems from CEC2020 and compared with advanced algorithms from the CEC2020 competition. The experimental results show that EO-HS-NDD performs well in solving real-world optimization problems.

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