Yazar "Ozkis, Ahmet" seçeneğine göre listele

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    A multi-objective artificial algae algorithm
    (ELSEVIER, 2018) Babalik, Ahmet; Ozkis, Ahmet; Uymaz, Sait Ali; Kiran, Mustafa Servet
    In this study, the authors focus on modification of the artificial algae algorithm (AAA), for multi-objective optimization. Basically, AAA is a population-based optimization algorithm inspired by the behavior of microalgae cells. In this work, a modified AAA with appropriate strategies is proposed for multi-objective Artificial Algae Algorithm (MOAAA) from the first AAA that was initially presented to solve single-objective continuous optimization problems. To the best of our knowledge, the MOAAA is the first modification of the AAA for solving multi-objective problems. Performance of the proposed algorithm is examined on a benchmark set consisting of 36 different multi-objective optimization problems and compared with four different swarm intelligence or evolutionary algorithms that are well-known in literature. The MOAAA is highly successful in solving multi-objective problems, and it has been demonstrated that the MOAAA is an alternative competitive algorithm in multi-objective optimization according to experimental results and comparisons presented in this research topic. (C) 2018 Elsevier B.V. All rights reserved.
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    A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm
    (ELSEVIER SCIENCE INC, 2017) Ozkis, Ahmet; Babalik, Ahmet
    This study investigates a multi-objective Vortex Search algorithm (MOVS) by modifying the single-objective Vortex Search algorithm or VS. The VS is a metaheuristic-based algorithm that uses a new adaptive step-size adjustment strategy to improve the performance of the search process. Search mechanism of the VS is inspired by the vortex pattern, so it is called a "Vortex Search" algorithm. The original VS is an improved way of solving single-objective continuous problems. To improve the MOVS algorithm, the VS algorithm is enhanced with added calculation approaches, such as fast-nondominated-sorting and crowding-distance, in order to identify the degree of non-dominance of the solutions and the densities of their occurrence. In addition, a crossover operation is added to the MOVS algorithm in order to enhance the Pareto front convergence capacity of the solutions. Finally, to spread the solutions more successfully over the Pareto front, it has been randomly produced using the inverse incomplete gamma function using a parameter between 0 and 1. The proposed MOVS algorithm is tested against 36 different benchmark problems together with NSGAII, MOCeII, IBEA and MOEA/D algorithms. The test results indicate that the MOVS algorithm achieves a better performance on accuracy and convergence speed than any other algorithms when comparisons are made against several test problems, and they also show that it is a competitive algorithm. (C) 2017 Elsevier Inc. All rights reserved.