IEICE TRANSACTIONS on Information
A Non-Revisiting Equilibrium Optimizer Algorithm
Summary :
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
Authors
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
Keyword
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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},}
Copy
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 -
English
