A hybrid membrane evolutionary algorithm for solving constrained optimization problems

Xiao, Jianhua; Huang, Yufang; Cheng, Zhen; He, Jifeng; Niu, Y.

doi:10.1016/j.ijleo.2013.08.032

Cited by 32 publications

(13 citation statements)

References 17 publications

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“…A recent approach (Long 2014) combined different constraint handling strategy and EAs to select the best combination. Another recent method proposed a hybrid EAs to handle the constraints (Xiao et al 2014). One-level membrane structure is combined with PSO.…”

Section: Related Studiesmentioning

confidence: 99%

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

Datta

Deb

2015

Soft Comput

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The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure. To address these, the present paper proposes a unified approach of constraint handling, which is capable of handling all inequality, equality and hybrid constraints in a coherent manner. The proposed method also automatically resolves the issue of constraint scaling which is critical in real world and engineering optimization problems. The proposed unified approach converts the single-objective constrained optimization problem into a multi-objective problem. Evolutionary multi-objective optimization is used to solve the modified bi-objective problem and to estimate the penalty parameter automatically. The constrained optimum is further improved using classical optimization. The efficiency of the proposed method is validated on a set of well-studied constrained test problems and compared against without using normalization technique to show the necessity of normalization. The results establish the importance of scaling , especially in constrained optimization and call for further investigation into its use in constrained optimization research.

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Section: Related Studiesmentioning

confidence: 99%

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

Datta

Deb

2015

Soft Comput

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“…Over the past years, a variety of membrane systems integrated with PSO have been proposed and proved powerful and efficient in solving optimization problems. Xiao et al [37] proposed a hybrid membrane evolutionary algorithm, which combines a one-level membrane structure with a PSO local search algorithm. Xiao et al [38] developed an improved dynamic membrane evolutionary algorithm based on PSO and DE to solve constrained engineering design problems.…”

Section: Introductionmentioning

confidence: 99%

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Wang

Liu

Sun

et al. 2020

Mathematical Problems in Engineering

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An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. e purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. is extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

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“…Until now, many kinds of P system have been proposed to solve NP-complete problems, such as SAT [9][10][11][12][13], HPP [14][15], Subset [16], Knapsack Problem [17], optimization problem [18][19][20]. There are two main ways for P systems solving NP-complete problem: the semi-uniform way, which associates with each instance of the problem one P system solving it, and the uniform way, which associates with each possible size of the instances of the problem one P system that can solve all instances of that size [21].…”

Section: Introductionmentioning

confidence: 99%

A P system for Hamiltonian cycle problem

Guo

Dai

Chen³

2016

Optik

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A hybrid membrane evolutionary algorithm for solving constrained optimization problems

Cited by 32 publications

References 17 publications

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

A P system for Hamiltonian cycle problem

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