Multi-objective optimization based on self-adaptive differential evolution algorithm

Huang, V.L.; Qin, A. K.; Suganthan, Ponnuthurai Nagaratnam; Taşgetiren, M. Fatih

doi:10.1109/cec.2007.4424939

Cited by 58 publications

(49 citation statements)

References 7 publications

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“…3.4 that new offspring creating mechanism plays an important role in accelerating the convergence speed in dMO. Suganthan [42] introduced that the GA based sMOEAs [36,41] always emerged top convergence speed within a low computational cost, while differential evolution (DE) based sMOEAs [28,35] can achieve well-distributed Pareto front after a long optimization process. In the new strategy, GA and DE are adopted serially with the purpose of benefitting from both GA and DE.…”

Section: New Offspring Creating Mechanism Powered By Adaptive Geneticmentioning

confidence: 99%

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Wang

2009

Memetic Comp.

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Dynamic optimization and multi-objective optimization have separately gained increasing attention from the research community during the last decade. However, few studies have been reported on dynamic multi-objective optimization (dMO) and scarce effective dMO methods have been proposed. In this paper, we fulfill these gabs by developing new dMO test problems and new effective dMO algorithm. In the newly designed dMO problems, Pareto-optimal decision values (i.e., Pareto-optimal solutions: POS) or both POS and Pareto-optimal objective values (i.e., Pareto-optimal front: POF) change with time. A new multi-strategy ensemble multi-objective evolutionary algorithm (MS-MOEA) is proposed to tackle the challenges of dMO. In MS-MOEA, the convergence speed is accelerated by the new offspring creating mechanism powered by adaptive genetic and differential operators (GDM); a Gaussian mutation operator is employed to cope with premature convergence; a memory like strategy is proposed to achieve better starting population when a change takes place. In order to show the advantages of the proposed algorithm, we experimentally compare MS-MOEA with several algorithms equipped with traditional restart strategy. It is suggested that such a multi-strategy ensemble approach is promising for dealing with dMO problems.

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Section: New Offspring Creating Mechanism Powered By Adaptive Geneticmentioning

confidence: 99%

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Wang

2009

Memetic Comp.

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“…The Multiobjective Self Adaptive Differential Evolution (MOSADE) [23] algorithm was used to generate the Pareto Front corresponding to 7 and the resulting front is shown in Figure 3. In doing so, we integrated within MOSADE, the PATH Solver from [9] to solve the CP (i.e.…”

Section: Case 2: Example 1 As a Moepecmentioning

confidence: 99%

An evolutionary algorithm based on Nash Dominance for Equilibrium Problems with Equilibrium Constraints

Koh

2012

Applied Soft Computing

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This paper introduces an evolutionary algorithm for the solution of a class of hierarchical ("leader-follower") games known as Equilibrium Problems with Equilibrium Constraints (EPECs). In one manifestation of such games, players at the upper level who assume the role of leaders, are assumed to act non cooperatively to maximise individual payoffs. At the same time, each leader's payoffs are constrained not only by their competitor's actions but also by the behaviour of the followers at the lower level which manifests in the form of an equilibrium constraint. By a redefinition of the selection criteria used in evolutionary methods, the paper demonstrates that the solution for such games can be found via a simple modification to a standard evolutionary multiobjective algorithm. We give a proposed algorithm (NDEMO) and illustrate it with numerical examples drawn from both the transportation systems management literature and the electricity generation industry underlying the applicability of NDEMO in multidisciplinary contexts.

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“…Other works define mutation operators by means of the combination of other existing ones. Some examples of these are Xue et al (2005); Yang et al (2005); Dong et al (2007); Teo et al (2007); Huang et al (2007b); Fu et al (2008); Chen and Lu (2008); Pant et al (2009).…”

Section: Introductionmentioning

confidence: 96%

A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms

Mateo

Alberto

2011

J Heuristics

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Evolutionary Algorithms, EA's, try to imitate, in some way, the principles of natural evolution and genetics. They evolve a population of potential solutions to the problem using operators such as mutation, crossover and selection. In general, the mutation operator is responsible for the diversity of the population and helps to avoid the problem of premature convergence to local optima (a premature stagnation of the search caused by the lack of population diversity).In this paper we present a new mutation operator in the context of Multi-Objective Evolutionary Algorithms, MOEA's, which makes use of the definition of Pareto optimality and manages the maximal amplitude or maximal step size of the mutation according to the Pareto layer of the individual and also of the iteration number. The behaviour of our mutation operator reveals that the use of variation operators which take into consideration the quality of the solutions, in terms of Pareto dominance or Pareto layers, can help to improve them. The Pareto based mutation operator proposed is compared with four well established and extensively used mutation operators: random mutation, non-uniform mutation, polynomial mutation and Gaussian mutation. The accomplished experiments reveal that our mutation operator performs, in most of the test problems considered, better than the others.

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Multi-objective optimization based on self-adaptive differential evolution algorithm

Cited by 58 publications

References 7 publications

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

An evolutionary algorithm based on Nash Dominance for Equilibrium Problems with Equilibrium Constraints

A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms

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