Multiobjective bio-inspired algorithm based on membrane computing

Zhao, Jinhui; Wang, Ning; Zhou, Ping

doi:10.1109/csip.2012.6308894

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…The two types of HMS could be thought as the hybridization of NMS and OLMS. In [98], two layers of OLMS were applied to design the membrane structure. In [81], NMS and a star topology were combined with DE to establish two variants of MIEAs, respectively.…”

Section: Hierarchical Structuresmentioning

confidence: 99%

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Evolutionary membrane computing: A comprehensive survey and new results

Zhang

Gheorghe

Pan

et al. 2014

Information Sciences

140

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Evolutionary membrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are receiving increasing attention. In this paper, an overview of the evolutionary membrane computing state-of-the-art and new results on two established topics in well defined scopes (membrane-inspired evolutionary algorithms and automated design of membrane computing models) are presented. We survey their theoretical developments and applications, sketch the differences between them, and compare the advantages and limitations.

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Section: Hierarchical Structuresmentioning

confidence: 99%

“…HMS-based MIEAs were applied to solve four sorts of problems. In [95,26,57,98], benchmark functions were discussed. In [26,27], optimal controllers for a marine diesel engine and a time-varying unstable plant were designed by applying a singleobjective and a dynamic multi-objective MIEA, respectively.…”

Section: Hierarchical Structuresmentioning

confidence: 99%

Evolutionary membrane computing: A comprehensive survey and new results

Zhang

Gheorghe

Pan

et al. 2014

Information Sciences

140

View full text Add to dashboard Cite

show abstract

“…24 25 As for application, a very few MIEAs were proposed for solving MOPs. [29][30][31][32] These algorithms were based on heavily studied membrane structures like NMS and OLMS and well established strategies like niching and crowding distance 33 from evolutionary multi-objective optimization community. Meanwhile, the performances of these algorithms on MOPs are not very competitive as compared with some well-known algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Membrane-Inspired Evolutionary Algorithm Based on Population P Systems and Differential Evolution for Multi-Objective Optimization

Cheng¹,

Zhang²,

Wang³

2015

Jnl of Comp & Theo Nano

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This paper proposes a membrane-inspired evolutionary algorithm based on population P systems and differential evolution for multi-objective optimization. In the algorithm, the cells of population P systems are divided into two groups. The first group, consisting of most of cells, focuses on evolving objects using differential evolution rules while the second group, consisting of only one cell, aims at selecting and re-distributing objects across the first group of cells for next generation using a special selection rule. Moreover, the communications among cells are performed at both local and global levels in order to obtain well converged and distributed solution set. Twelve benchmark problems with diverse features are utilized to test algorithm performance. Experimental results show that the proposed approach outperforms five well-known algorithms in terms of three performance metrics.

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