Many-Objective Brain Storm Optimization Algorithm

Wu, Yue; Wang, Xinrui; Fu, Yixiao; Li, Guoting

doi:10.1109/access.2019.2960874

Cited by 12 publications

(6 citation statements)

References 56 publications

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“…Each solution is regarded as a feasible solution, and the process of obtaining the optimal solution through discussion and integration. It has the advantages of good stability, high accuracy, and fast convergence [21]. In view of this, this paper uses the BSO algorithm to solve the multi-objective optimization problem of electrically coupled system operation.…”

Section: Solving Of the Optimization Problemmentioning

confidence: 99%

Data Mining Based Integrated Electric-Gas Energy System Multi-Objective燨ptimization

Tan¹,

Ren²,

Li³

et al. 2022

Energy Engineering

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With the proposal of carbon neutrality, how to improve the proportion of clean energy in energy consumption and reduce carbon dioxide emissions has become the important challenge for the traditional energy industry. Based on the idea of multi-energy complementarity, a typical integrated energy system consisting of electric system and gas system is constructed based on the application of power to gas (P2G) technology and gas turbine in this paper. Furthermore, a multi-objective optimization model with economic improvement, carbon emission reduction and peak-load shifting as objectives is proposed, and solved by BSO algorithm. Finally, a typical power-gas coupling system is selected as an example to verify the effectiveness of the model. The results showed that the proposed multi-objective optimization model based on BSO algorithm can better play the complementary characteristics of the electric and gas system, and significantly improve the comprehensive benefits of system operation.

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Section: Solving Of the Optimization Problemmentioning

confidence: 99%

Data Mining Based Integrated Electric-Gas Energy System Multi-Objective燨ptimization

Tan¹,

Ren²,

Li³

et al. 2022

Energy Engineering

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“…BSO is a new EA proposed in recent years, and it uses all possible individuals to update the population, so as to good diversity, which is especially beneficial to solve multi-objective optimization problems. Some multi-objective BSO algorithms have been proposed [21]- [24], and perform well on a wide range of multi-objective optimization problems. And a novel many-objective algorithm based on BSO was proposed to deal with the many-objective UDS optimization model.…”

Section: Many-objective Bso Optimization Algorithmmentioning

confidence: 99%

An Integrated Decision-Making Framework Based on Many-Objective Brain Storming Optimization for Urban Drainage System Design

Zheng

Wang

et al. 2022

IEEE Access

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Many cities around the world face the flooding management problems as extreme rainfall events have become more frequent. It's of great practical significance to design an effective urban drainage system (UDS) to improve the stormwater runoff quality. An integrated decision-making analysis framework based on many-objective brain storm optimization is proposed to make the optimal design of UDS. Firstly, a generic mathematical model is presented considering five objectives simultaneously. Secondly, an effective many-objective brain storm optimization based on local region dominance (DRP) is designed as the solver of the model and a set of Pareto-optimal solutions are obtained. Thirdly, two kinds of multi-attribute decision analysis methods are introduced to rank the Pareto-optimal solutions. Fourthly, the comparison experiments of a case study with three multi-objective evolutionary algorithms show the promising performance of the integrated decision-making framework and the corresponding optimization algorithm.

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“…Zhao et al developed a decomposition-based many-objective artificial bee colony algorithm with reinforcement learning 13 . Wu et al introduced a novel many-objective Brain Storm optimization algorithm 14 . Zhang et al proposed a hybrid multi-agent Coordination Optimization Algorithm (MCO) 15 that, applies a coordination mechanism to accelerate convergence.…”

Section: Introductionmentioning

confidence: 99%

An improved farmland fertility algorithm for many-objective optimization problems

Wang

Gao

Chen

2022

Sci Rep

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Recent studies on many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, difficulties in scalability upon MaOPs including the selection of leaders, etc., are encountered because the most evolutionary algorithms are proposed for single-objective optimization. To further improve the performance of many-objective evolutionary algorithms in solving MaOPs when the number of the objectives increases, this paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). In MOIFF, a novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy. In order to handle MaOPs effectively, FF has been tailored from the following aspects. An individual fitness assessment approach based on cumulative ranking value has been proposed to distinguish the quality of each individual; a novel method based on individual cumulative ranking value to constitute and update the global memory and local memory of each individual is proposed, and a hybrid subspace search and full space search method has been designed to update individuals in the stages of soil optimization and soil fusion. In addition, adaptive environmental selection has been proposed. Finally, MOIFF is compared with four state-of-the art many-objective evolutionary algorithms on many test problems with various characteristics, including the DTLZ and WFG test suites. Experimental results demonstrate that the proposed algorithm has competitive convergence and diversity on MaOPs.

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Many-Objective Brain Storm Optimization Algorithm

Cited by 12 publications

References 56 publications

Data Mining Based Integrated Electric-Gas Energy System Multi-Objective燨ptimization

Data Mining Based Integrated Electric-Gas Energy System Multi-Objective燨ptimization

An Integrated Decision-Making Framework Based on Many-Objective Brain Storming Optimization for Urban Drainage System Design

An improved farmland fertility algorithm for many-objective optimization problems

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