Multi-objective learning backtracking search algorithm for economic emission dispatch problem
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METHODOLOGIES AND APPLICATION
Multi-objective learning backtracking search algorithm for economic emission dispatch problem Xinlin Xu1 · Zhongbo Hu2 · Qinghua Su2 · Zenggang Xiong3 · Mianfang Liu4
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The backtracking search algorithm (BSA) as a novel intelligent optimizer belongs to population-based evolutionary algorithms. In this paper, a multi-objective learning backtracking search algorithm (MOLBSA) is proposed to solve the environmental/economic dispatch (EED) problem. In this algorithm, we design two novel learning strategies: a leader-choosing strategy, which takes a sparse solution from an external archive as leader; a leader-guiding strategy, which updates individuals with the guidance of leader. These two learning strategies have outstanding performance in improving the uniformity and diversity of obtained Pareto front. The extreme solutions, compromise solution and three metrics obtained by MOLBSA are further compared with those of well-known multi-objective optimization algorithms in IEEE 30-bus 6-unit test system and 10unit test system. Simulation results demonstrate the capability of MOLBSA in generating well-distributed and high-quality approximation of true Pareto front for the EED problem. Keywords Backtracking search algorithm · Environmental/economic dispatch · Multi-objective optimization
1 Introduction The backtracking search algorithm (BSA) (Civicioglu 2013), a population-based evolutionary algorithm (EA), was first proposed by Civicioglu in 2013 as a novel approach to Communicated by V. Loia.
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Zhongbo Hu [email protected] Xinlin Xu [email protected] Qinghua Su [email protected] Zenggang Xiong [email protected] Mianfang Liu [email protected]
1
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, China
2
School of Information and Mathematics, Yangtze University, Jingzhou, Hubei, China
3
School of Computer and Information Science, Hubei Engineering University, Xiaogan, Hubei, China
4
School of Software and Information Engineering, Hunan Software Vocational Institute, Xiangtan, Hunan, China
solve nonlinear, non-differentiable and multi-modal numerical optimization problems. Compared with some similar evolutionary algorithms, BSA has only one single control parameter (mi xrate). More particularly, BSA possesses a memory called old population to provide search direction for the mutation, which stores a population randomly selected from the current generation or a previous generation. Over the past few years, there have been some successful applications of BSA in various fields (Wang et al. 2019; Pare et al. 2018; Mohd Zain et al. 2018; Hannan et al. 2018; Abdolrasol et al. 2018). It is widely used in engineering fields, such as power system (Modiri-Delshad and Rahim 2014; Ali 2015; ModiriDelshad et al. 2016; Pal et al. 2016; Modiri-Delshad et al. 2016; Dubey et al. 2016; Bhattacharjee et al. 2015; ModiriDelshad and Rahim 2016; Kılıç 2015; Ayan and Kılıç 2016; Chaib et a
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