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The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.3 pp.365-373

- Publication Date
- 2023/03/01

- Publicized
- 2022/12/20

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.2022EDP7119

- Type of Manuscript
- PAPER

- Category
- Artificial Intelligence, Data Mining

Baohang ZHANG

University of Toyama

Haichuan YANG

University of Toyama

Tao ZHENG

University of Toyama

Rong-Long WANG

University of Fukui

Shangce GAO

University of Toyama

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Baohang ZHANG, Haichuan YANG, Tao ZHENG, Rong-Long WANG, Shangce GAO, "A Non-Revisiting Equilibrium Optimizer Algorithm" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 3, pp. 365-373, March 2023, doi: 10.1587/transinf.2022EDP7119.

Abstract: The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7119/_p

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@ARTICLE{e106-d_3_365,

author={Baohang ZHANG, Haichuan YANG, Tao ZHENG, Rong-Long WANG, Shangce GAO, },

journal={IEICE TRANSACTIONS on Information},

title={A Non-Revisiting Equilibrium Optimizer Algorithm},

year={2023},

volume={E106-D},

number={3},

pages={365-373},

abstract={The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.},

keywords={},

doi={10.1587/transinf.2022EDP7119},

ISSN={1745-1361},

month={March},}

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TY - JOUR

TI - A Non-Revisiting Equilibrium Optimizer Algorithm

T2 - IEICE TRANSACTIONS on Information

SP - 365

EP - 373

AU - Baohang ZHANG

AU - Haichuan YANG

AU - Tao ZHENG

AU - Rong-Long WANG

AU - Shangce GAO

PY - 2023

DO - 10.1587/transinf.2022EDP7119

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E106-D

IS - 3

JA - IEICE TRANSACTIONS on Information

Y1 - March 2023

AB - The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

ER -