Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism

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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“…One-level membrane structure is combined with PSO. Mazhoud et al (2013) also used PSO to handle constraints in single-objective optimization.…”

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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“…Compared with other evolutionary algorithms, PSO is easier to implement and has an excellent performance on various benchmark problems. As a result, PSO has been successfully applied in solving many realworld optimization problems (Eslami et al 2014;Liu et al 2014;Mazhoud et al 2013;Roy et al 2014;Tsai et al 2014;, Zhao et al 2014a, Idris et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Ecosystem particle swarm optimization

Liu

et al. 2016

Soft Comput

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Particle swarm optimization (PSO) is a wellknown swarm intelligence algorithm inspired by the foraging behavior of bird flocking. PSO has been widely used in many optimization and engineering problems due to its simplicity and efficiency, even though there still exist some disadvantages. The standard PSO often suffers with premature convergence or slow convergence when the optimization problem is multimodal or high-dimensional. To overcome these drawbacks, an ecosystem PSO (ESPSO) inspired by the characteristic that a natural ecosystem can excellently keep the biological diversity and make the whole ecosystem be in a dynamic balance is presented in this paper. ESPSO not only prevents the algorithm trapping into local optima but also balances the exploration and exploitation in both unimodal and multimodal problems as compared to other PSO variants. Twenty benchmark functions including unimodal functions and multimodal nonlinear functions are used to test the searching ability of ESPSO. Experimental results show that ESPSO considerably improves the searching accuracy, the algorithm reliability and the searching efficiency in comparison with other six well-known PSO variants and four evolutionary algorithms. Moreover, ESPSO was successfully Communicated by A. applied to the antenna array pattern synthesis design and gained satisfactory results.

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“…The experiments consisted of: (a) solving benchmark optimization problems and comparing the results with the ones achieved by CiP-PSO [7], COPSO [1], CVI-PSO [28] and IAPSO [5]; (b) verifying the number of runs that were able to achieve global optimum or a value close to it; and (c) verifying the amount of unfeasible space that was removed by [I]PSO for each problem.…”

Section: Methodsmentioning

confidence: 99%

“…As an example, a mutation operator similar to the one used in genetic algorithms may be included at each iteration [19,3,21,27]. Mazhoud et al [28] present a constraint handling mechanism that consists of a closeness evaluation of the solutions to the feasible region, and uses Interval Arithmetic to normalize the total evaluations.…”

Section: Constrained Optimization Using Psomentioning

confidence: 99%

An interval space reducing method for constrained problems with particle swarm optimization

Machado-Coelho

Machado

Jaulin

et al. 2017

Applied Soft Computing

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In this paper, we propose a method for solving constrained optimization problems using Interval Analysis combined with Particle Swarm Optimization. A Set Inverter Via Interval Analysis algorithm is used to handle constraints in order to reduce constrained optimization to quasi unconstrained one. The algorithm is useful in the detection of empty search spaces, preventing useless executions of the optimization process. To improve computational efficiency, a Space Cleaning algorithm is used to remove solutions that are certainly not optimal. As a result, the search space becomes smaller at each step of the optimization procedure. After completing pre-processing, a modified Particle Swarm Optimization algorithm is applied to the reduced search space to find the global optimum. The efficiency of the proposed approach is demonstrated through comprehensive experimentation involving 100,000 runs on a set of wellknown benchmark constrained engineering design problems. The computational efficiency of the new method is quantified by comparing its results with other PSO variants found in the literature.

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Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism

Cited by 104 publications

References 26 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

Ecosystem particle swarm optimization

An interval space reducing method for constrained problems with particle swarm optimization

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