Applying family competition to evolution strategies for constrained optimization

Yang, Jinn-Moon; Chen, Ying-ping; Horng, Jorng-Tzong; Kao, Cheng−Yan

doi:10.1007/bfb0014812

Cited by 79 publications

(39 citation statements)

References 16 publications

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“…Therefore, depending on the mathematical properties of the N-dimensional function R, the convergence can slow down, especially if the solution is in a narrow wells. The classical recombination uses fixed weights (Donnel & Waagen, 1995) or uniformly distributed weights for each element (Yang et al, 1997 …”

Section: Evolutionary Method: a Selective Methodsmentioning

confidence: 99%

Numerical Optimization of Plasmonic Biosensors

Barchiesi¹

2011

New Perspectives in Biosensors Technology and Applications

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Section: Evolutionary Method: a Selective Methodsmentioning

confidence: 99%

Numerical Optimization of Plasmonic Biosensors

Barchiesi¹

2011

New Perspectives in Biosensors Technology and Applications

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“…Since PSO was developed primarily as an unconstrained optimization algorithm, we penalize the constraints by a stationary multi-stage penalty function [20] in the objective function to transform LCP to an unconstrained problem:…”

Section: Pso For Lcpmentioning

confidence: 99%

LP Guided PSO Algorithm for Office Lighting Control

Pan

Ogai

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn most existing centralized lighting control systems, the lighting control problem (LCP) is reformulated as a constrained minimization problem and solved by linear programming (LP). However, in realworld applications, LCP is actually discrete and non-linear, which means that more accurate algorithm may be applied to achieve improvements in energy saving. In this paper, particle swarm optimization (PSO) is successfully applied for office lighting control and a linear programming guided particle swarm optimization (LPPSO) algorithm is developed to achieve considerable energy saving while satisfying users' lighting preference. Simulations in DIALux office models (one with small number of lamps and one with large number of lamps) are made and analyzed using the proposed control algorithms. Comparison with other widely used methods including LP shows that LPPSO can always achieve higher energy saving than other lighting control methods.

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“…By penalizing the constraints and building a single objective function, the constrained problem can be transformed into an unconstrained one, which is in turn maximized using an unconstrained optimization algorithm. A penalty function is generally defined as [30]:…”

Section: Penalty Functionmentioning

confidence: 99%

“…Step 1: Initialization for i=1 to S sensingSpectrum(i) end for calculate | k | for all k according to (28); generate base particle according to Algorithm 1 generate particle and shadow particle according to (30) (31) Step 2: Subchannel Allocation for i=1 to N pso calculate the fitness(i) according to (32) end for renew G best ,Gparticle,P i best and Pparticle i ∀i renew velocity i and particle i ∀i according to (33) (34) if satisfy the stop conditions go to Step 3 else go to the top of Step 2; end if…”

Section: Algorithm 2 Pso-based Adaptive Resource Allocation Algorithmmentioning

confidence: 99%

Adaptive resource allocation for cognitive radio networks with multiple primary networks

Wang

Zhang

et al. 2012

J Wireless Com Network

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In this article, adaptive resource allocation (ARA) is investigated for multiple primary networks based-cognitive radio networks under a more practical system model, where the bandwidth of each secondary user is assumed to be limited and the maximum allowable interference for each primary network is different. We first formulate the ARA as a constrained optimization problem with the objective function of maximizing the proportional fairness-based ergodic sum capacity. The multiple constraints optimization problem is NP-hard and therefore, we propose a scheme to decompose the optimization problem into two unconstrained optimization problems by designing alternative objective functions and penalty functions. Then, a suboptimal heuristic solution framework based on particle swarm optimization is proposed to solve the unconstrained optimization problems. Computation simulations are carried out and the results show that the proposed scheme outperforms traditional ARA schemes.

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Applying family competition to evolution strategies for constrained optimization

Cited by 79 publications

References 16 publications

Numerical Optimization of Plasmonic Biosensors

Numerical Optimization of Plasmonic Biosensors

LP Guided PSO Algorithm for Office Lighting Control

Adaptive resource allocation for cognitive radio networks with multiple primary networks

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