Brain storm optimization algorithm for multi-objective optimization problems

Jingqian Xue; Yali Wu; Yuhui Shi; Shi Cheng

doi:10.1007/978-3-642-30976-2_62

Brain storm optimization algorithm for multi-objective optimization problems

Jingqian Xue^*, Yali Wu, Yuhui Shi, Shi Cheng

^*Corresponding author for this work

Department of Electrical and Electronic Engineering

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

74 Citations (Scopus)

Abstract

In this paper, a novel multi-objective optimization algorithm based on the brainstorming process is proposed(MOBSO). In addition to the operations used in the traditional multi-objective optimization algorithm, a clustering strategy is adopted in the objective space. Two typical mutation operators, Gaussian mutation and Cauchy mutation, are utilized in the generation process independently and their performances are compared. A group of multi-objective problems with different characteristics were tested to validate the effectiveness of the proposed algorithm. Experimental results show that MOBSO is a very promising algorithm for solving multi-objective optimization problems.

Original language	English
Title of host publication	Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings
Pages	513-519
Number of pages	7
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-30976-2_62
Publication status	Published - 2012
Event	3rd International Conference on Swarm Intelligence, ICSI 2012 - Shenzhen, China Duration: 17 Jun 2012 → 20 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7331 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Swarm Intelligence, ICSI 2012
Country/Territory	China
City	Shenzhen
Period	17/06/12 → 20/06/12

Keywords

Brain Strom Algorithm
Clustering Strategy
Multi-objective Optimization
Mutation Operator

Access to Document

10.1007/978-3-642-30976-2_62

Cite this

Xue, J., Wu, Y., Shi, Y., & Cheng, S. (2012). Brain storm optimization algorithm for multi-objective optimization problems. In Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings (PART 1 ed., pp. 513-519). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7331 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-30976-2_62

Xue, Jingqian ; Wu, Yali ; Shi, Yuhui et al. / Brain storm optimization algorithm for multi-objective optimization problems. Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings. PART 1. ed. 2012. pp. 513-519 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Brain storm optimization algorithm for multi-objective optimization problems",

abstract = "In this paper, a novel multi-objective optimization algorithm based on the brainstorming process is proposed(MOBSO). In addition to the operations used in the traditional multi-objective optimization algorithm, a clustering strategy is adopted in the objective space. Two typical mutation operators, Gaussian mutation and Cauchy mutation, are utilized in the generation process independently and their performances are compared. A group of multi-objective problems with different characteristics were tested to validate the effectiveness of the proposed algorithm. Experimental results show that MOBSO is a very promising algorithm for solving multi-objective optimization problems.",

keywords = "Brain Strom Algorithm, Clustering Strategy, Multi-objective Optimization, Mutation Operator",

author = "Jingqian Xue and Yali Wu and Yuhui Shi and Shi Cheng",

year = "2012",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Xue, J, Wu, Y, Shi, Y & Cheng, S 2012, Brain storm optimization algorithm for multi-objective optimization problems. in Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7331 LNCS, pp. 513-519, 3rd International Conference on Swarm Intelligence, ICSI 2012, Shenzhen, China, 17/06/12. https://doi.org/10.1007/978-3-642-30976-2_62

Brain storm optimization algorithm for multi-objective optimization problems. / Xue, Jingqian; Wu, Yali; Shi, Yuhui et al.
Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings. PART 1. ed. 2012. p. 513-519 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7331 LNCS, No. PART 1).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Xue, Jingqian

AU - Wu, Yali

AU - Shi, Yuhui

AU - Cheng, Shi

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N2 - In this paper, a novel multi-objective optimization algorithm based on the brainstorming process is proposed(MOBSO). In addition to the operations used in the traditional multi-objective optimization algorithm, a clustering strategy is adopted in the objective space. Two typical mutation operators, Gaussian mutation and Cauchy mutation, are utilized in the generation process independently and their performances are compared. A group of multi-objective problems with different characteristics were tested to validate the effectiveness of the proposed algorithm. Experimental results show that MOBSO is a very promising algorithm for solving multi-objective optimization problems.

AB - In this paper, a novel multi-objective optimization algorithm based on the brainstorming process is proposed(MOBSO). In addition to the operations used in the traditional multi-objective optimization algorithm, a clustering strategy is adopted in the objective space. Two typical mutation operators, Gaussian mutation and Cauchy mutation, are utilized in the generation process independently and their performances are compared. A group of multi-objective problems with different characteristics were tested to validate the effectiveness of the proposed algorithm. Experimental results show that MOBSO is a very promising algorithm for solving multi-objective optimization problems.

KW - Brain Strom Algorithm

KW - Clustering Strategy

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KW - Mutation Operator

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Xue J, Wu Y, Shi Y, Cheng S. Brain storm optimization algorithm for multi-objective optimization problems. In Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings. PART 1 ed. 2012. p. 513-519. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-30976-2_62

Brain storm optimization algorithm for multi-objective optimization problems

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