Comparing a coevolutionary genetic algorithm for multiobjective optimization

Lohn, Jason; Kraus, W.F.; Haith, Gary

doi:10.1109/cec.2002.1004406

Cited by 54 publications

(39 citation statements)

References 4 publications

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“…A Multi Objective GA (MOGA) is proposed to solve multi objective problems combining both continuous and discrete variables [8,11]. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions [9].…”

Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

“…A solution is Pareto-optimal if no other solution can improve one objective function without a simultaneous deterioration of at least one of the other objectives. A set of such solutions is called the Pareto-optimal front [10,11]. An example of a Pareto front is seen in Figure 2.Evolutionary algorithms (EAs) have recently attracted much attention in the exploration of Paretooptimal fronts.…”

Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

“…The goal of course is to find the best or optimal solutions to the optimization problem at hand. Multi-objective genetic algorithm (MOGA) being a population-based approach, genetic algorithm (GA) are suited to solve multi-objective optimization problems [11,15]. A generic single-objective GA can be modified to find a set of multiple non-dominated solutions in a single run [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms

Chand¹,

Mohanty²

2013

IJCA

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Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multi objective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm for big problem gives less efficient results, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce the complexity. Though successful in giving good results but, there is scope for further improvements, especially considering that the populations involved are frequently of large size. We propose a variant which gives better efficient results. The improved algorithm is applied to the transportation problem: vehicle routing problem (VRP). Results of comparative tests are presented showing that the improved algorithm performs well on large populations.

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Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms

Chand¹,

Mohanty²

2013

IJCA

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“…In brief, the competitive one is supposed when individuals are rewarded if they defeat the individuals with which they compete. These interactions can support "arms races" in which the individuals force each other to become increasingly competent [21]. The instances are the predator-prey kind of relations.…”

Section: Parallel Evolutionmentioning

confidence: 99%

Synthesis of Time-to-Amplitude Converter by Mean CoeVolution with Adaptive Parameters

Sapargaliyev¹,

Kalganova

2011

JSEA

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“…Decision support system is an interactive computer-based system to support user in assessing and deciding an option [6]. The applicability and efficiency of solution procedure is demonstrated through numerical example and a co-evolutionary algorithms and genetic algorithm (GA) is proposed as a solution method [7] Background and Related Works:-A study of coevolutionary algorithm and genetic algorithm for multiobjective optimization is performed used in the previous research by [3]. In their study, they were comparing coevolutionary algorithm (CGA) with 7 other algorithm in optimizing multiobjective function.…”

Section: …………………………………………………………………………………………………… Introduction:-mentioning

confidence: 99%