A niching co-swarm gravitational search algorithm for multi-modal optimization

Anupam Yadav; Joong Hoon Kim

doi:10.1007/978-81-322-2217-0_48

A niching co-swarm gravitational search algorithm for multi-modal optimization

Anupam Yadav, Joong Hoon Kim

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

In this paper, a Niching co-swarm gravitational search algorithm (CoGSA) is designed for solving multi-modal optimization problems. The collective approach of Gravitational Search Algorithm and differential evolution (DE) is used to solve multi-modal optimization problems. A set of twelve multi-modal problems are taken from a benchmark set of CEC 2013. An experimental study has been performed to evaluate the availability of CoGSA over these twelve problems. The performance is measured in an advanced way. It has been observed that CoGSA provides good solution for multi-modal optimization problems.

Original language	English
Pages (from-to)	599-607
Number of pages	9
Journal	Advances in Intelligent Systems and Computing
Volume	335
DOIs	https://doi.org/10.1007/978-81-322-2217-0_48
Publication status	Published - 2015

Keywords

Differential evolution
Gravitational search algorithm
Multi-modal
Optimization

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science(all)

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10.1007/978-81-322-2217-0_48

Cite this

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title = "A niching co-swarm gravitational search algorithm for multi-modal optimization",

abstract = "In this paper, a Niching co-swarm gravitational search algorithm (CoGSA) is designed for solving multi-modal optimization problems. The collective approach of Gravitational Search Algorithm and differential evolution (DE) is used to solve multi-modal optimization problems. A set of twelve multi-modal problems are taken from a benchmark set of CEC 2013. An experimental study has been performed to evaluate the availability of CoGSA over these twelve problems. The performance is measured in an advanced way. It has been observed that CoGSA provides good solution for multi-modal optimization problems.",

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AU - Kim, Joong Hoon

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N2 - In this paper, a Niching co-swarm gravitational search algorithm (CoGSA) is designed for solving multi-modal optimization problems. The collective approach of Gravitational Search Algorithm and differential evolution (DE) is used to solve multi-modal optimization problems. A set of twelve multi-modal problems are taken from a benchmark set of CEC 2013. An experimental study has been performed to evaluate the availability of CoGSA over these twelve problems. The performance is measured in an advanced way. It has been observed that CoGSA provides good solution for multi-modal optimization problems.

AB - In this paper, a Niching co-swarm gravitational search algorithm (CoGSA) is designed for solving multi-modal optimization problems. The collective approach of Gravitational Search Algorithm and differential evolution (DE) is used to solve multi-modal optimization problems. A set of twelve multi-modal problems are taken from a benchmark set of CEC 2013. An experimental study has been performed to evaluate the availability of CoGSA over these twelve problems. The performance is measured in an advanced way. It has been observed that CoGSA provides good solution for multi-modal optimization problems.

KW - Differential evolution

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KW - Optimization

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A niching co-swarm gravitational search algorithm for multi-modal optimization

Abstract

Keywords

ASJC Scopus subject areas

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