Real-Parameter Optimization Using the Mutation Step Co-evolution

Pošík, Petr

doi:10.1109/cec.2005.1554775

Cited by 44 publications

(18 citation statements)

References 3 publications

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“…When comparing our results with other optimization techniques within the CEC 2005 benchmark problems, our distributed PSO versions give comparable results as CoEVO algorithm [50]. They also obtained the global optimum in functions 1 , 2 , and 7 .…”

Section: Discussionsupporting

confidence: 53%

Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

Jakubcová

Máca

Pech

2015

Mathematical Problems in Engineering

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The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO) algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

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Section: Discussionsupporting

confidence: 53%

Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

Jakubcová

Máca

Pech

2015

Mathematical Problems in Engineering

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“…Coevolution of mutation step size was proposed in [12]. This algorithm used a two-population approach, with one population encoding solutions to the target problem and the other encoding offset vectors.…”

Section: Coevolutionmentioning

confidence: 99%

Supportive coevolution

Goldman

Tauritz

2012

Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation

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Automatically configuring and dynamically controlling an Evolutionary Algorithm's (EA's) parameters is a complex task, yet doing so allows EAs to become more powerful and require less problem specific tuning to become effective. Supportive Coevolution is a new form of Evolutionary Algorithm (EA) that uses multiple populations to overcome the limitations of other automatic configuration techniques like self-adaptation, giving it the potential to concurrently evolve all of the parameters and operators in an EA.As a proof of concept experimentation comparing selfadaptation of n uncorrelated mutation step sizes with Supportive Coevolution for mutation step sizes was performed on the Rastrigin and Shifted Rastrigin benchmark functions. Statistical analysis showed Supportive Coevolution outperforming self-adaptation on all but one of the problem instances tested. Furthermore, analysis of instantaneous mutation success rate showed that this new technique is better able to adapt to the changes in the population fitness. Further study using multiple evolving parameters is needed to fully test Supportive Coevolution, but the results presented here show a promising outlook.

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“…CoEVO is an evolutionary algorithm for real parameter optimization. It uses a cooperative co-evolution to breed and reproduce successful mutation steps (Posik 2005). DE is a simple Differential Evolution algorithm (Rönkkönen et al 2005).…”

Section: Algorithms Used For Comparisonsmentioning

confidence: 99%

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

2009

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This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologicallyinspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles' behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.

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Real-Parameter Optimization Using the Mutation Step Co-evolution

Cited by 44 publications

References 3 publications

Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

Supportive coevolution

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

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