This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrainedoptimization proble...
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ISBN:
(纸本)9781424481262
This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrainedoptimization problems. It is well known that the performance of DE is sensitive to the choice of mutation strategies and associated control parameters. For these reasons, the ensemble is achieved in such a way that each individual is assigned to one of the two distinct mutation strategies or a variable parameter search (VPS). The algorithm was tested using benchmark instances in Congress on Evolutionary Computation 2010. For these benchmark problems, the problem definition file, codes and evaluation criteria are available in http://***/home/EPNSugan. Since the optimal or best known solutions are not available in the literature, the detailed computational results required in line with the special session format are provided for the competition.
In this paper, a novel algorithm called GCK is proposed to solve constrained function optimization problems. GCK proposes a new method called Two-time evolution to transform the equality constraints t
In this paper, a novel algorithm called GCK is proposed to solve constrained function optimization problems. GCK proposes a new method called Two-time evolution to transform the equality constraints t
A novel multi-population evolutionary algorithm (MPEA) is presented, which can solve the constrained function optimization problems rather efficiently. The MPEA adopts three populations with different multi-parent cro...
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ISBN:
(纸本)0387283188
A novel multi-population evolutionary algorithm (MPEA) is presented, which can solve the constrained function optimization problems rather efficiently. The MPEA adopts three populations with different multi-parent crossover operators. So each population emphasizes particularly on different searching regions and the complementarity of these three crossover operators can enhances the diversity of individuals, which improves the search ability of the MPEA dramatically. And during the MPEA runs, the three populations exchange the best solution in each generation to adjust its search direction to the possible optimum solution. Experiments have been carried on several benchmark functions to test the performance of the presented MPEA. Numerical results show that MPEA is highly competitive with other algorithms in effectiveness and generality.
Hybrid air-cushion vehicles (ACVs) provide a solution to transportation on soft terrain, whereas they also bring a new problem of excessive energy consumption. In order to minimize energy consumption in a computationa...
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ISBN:
(纸本)9783037855454
Hybrid air-cushion vehicles (ACVs) provide a solution to transportation on soft terrain, whereas they also bring a new problem of excessive energy consumption. In order to minimize energy consumption in a computational manner, a Genetic Algorithm (GA) - Neural Network (NN) joint optimization algorithm is proposed to online calculate the corresponding optimal vehicle running parameters for given soil conditions. This is realized in three steps. (1) The energy consumption index is firstly simplified as a constrainedfunction with respect to only two independent vehicle running parameters. (2) The optimal solutions are figured out offline with respect to some specific soil conditions by the designed GA optimizer. (3) The optimal solutions are figured out online with respect to general soil conditions by the designed NN optimizer, which is trained using the above offline-obtained data. The feasibility of the joint algorithm is supported by experiments, whose results show an effective integration of the GA's advantage in complex functionoptimization and the NN's advantage in generalization ability and computing speed.
A novel multi-population evolutionary algorithm(MPEA) is presented,which can solve the constrained function optimization problems rather *** MPEA adopts three populations with different multi-parent crossover *** each...
详细信息
A novel multi-population evolutionary algorithm(MPEA) is presented,which can solve the constrained function optimization problems rather *** MPEA adopts three populations with different multi-parent crossover *** each population emphasizes particularly on different searching regions and the complementarity of these three crossover operators can enhances the diversity of individuals,which improves the search ability of the MPEA *** during the MPEA runs,the three populations exchange the best solution in each generation to adjust its search direction to the possible optimum *** have been carried on several benchmark functions to test the performance of the presented *** results show that MPEA is highly competitive with other algorithms in effectiveness and generality.
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