The paper deals with testing of selected heuristic optimization methods and their evaluation. We have proposed different techniques which express the success of the optimization method in different ways (the method su...
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The paper deals with testing of selected heuristic optimization methods and their evaluation. We have proposed different techniques which express the success of the optimization method in different ways (the method success, the difference between optimum and local extreme, the distances of quartiles, the number of simulation experiments until the optimum was found). These evaluation techniques use box plot characteristics calculated from the repeated optimization experiments. (C) 2014 The Authors. Published by Elsevier Ltd.
The paper deals with testing of selected heuristic optimization methods and their evaluation. We have proposed different techniques which express the success of the optimization method in different ways (the method su...
详细信息
The paper deals with testing of selected heuristic optimization methods and their evaluation. We have proposed different techniques which express the success of the optimization method in different ways (the method success, the difference between optimum and local extreme, the distances of quartiles, the number of simulation experiments until the optimum was found). These evaluation techniques use box plot characteristics calculated from the repeated optimization experiments.
Modified magnetic field integral equations (MFIEs) are proposed incorporating a testing integration in the direction normal to the boundary of the target surface. The result,involves a simpler kernel than the conventi...
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Modified magnetic field integral equations (MFIEs) are proposed incorporating a testing integration in the direction normal to the boundary of the target surface. The result,involves a simpler kernel than the conventional MFIE, facilitating accurate computations. By employing a complex exponential testing function, the equations can be made immune to the interior resonance problem. The influence of the limits of integration, and the overall accuracy, is investigated using high order basis functions.
The beluga whale optimization (BWO) algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population shari...
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The beluga whale optimization (BWO) algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation, and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization (MBWO) with a multi-strategy. It was inspired by beluga whales' two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group aggregation strategy (GAs) and a migration strategy (Ms). The GAs can improve the local development ability of the algorithm and accelerate the overall rate of convergence through the group aggregation fine search;the Ms randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO's ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems. Graphical Abstract
As a new branch of natural computing, membrane computing has received increasing attention. The hierarchical and parallel structure of P system provides benefits for the resolving of optimization problems. In this pap...
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ISBN:
(纸本)9783642342882
As a new branch of natural computing, membrane computing has received increasing attention. The hierarchical and parallel structure of P system provides benefits for the resolving of optimization problems. In this paper, we combined membrane computing and evolutionary algorithms, and proposed an optimization algorithm to resolve the multi-variable optimization problems with constraints. The two standard testing functions were adopted to evaluate the proposed optimization algorithm. The results of the experiments showed the effectiveness of the proposed method.
The bee immune evolutionary algorithm was proposed in order to improve effectively the optimal ability of bee evolutionary genetic algorithm. In the evolutionary process of bee, the algorithm made on immune evolutiona...
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ISBN:
(纸本)9783037851555
The bee immune evolutionary algorithm was proposed in order to improve effectively the optimal ability of bee evolutionary genetic algorithm. In the evolutionary process of bee, the algorithm made on immune evolutionary iteration calculation, generate next-generation population, in the proportions of fitness values for the best individual and second-best individuals in each generation. Because the algorithm takes in the neighborhood of space search as well out the neighborhood of space search for the some optimal individuals, meanwhile, with iterative numbers increase, capability of local search can be strengthened gradually;the bee immune evolutionary algorithm can approach the global optimal solution with higher accuracy. The calculated results for typical best functions show that the bee immune evolutionary algorithm has better optimal capability and stability.
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