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- W2993035901 abstract "Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated." @default.
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- W2993035901 date "2019-11-22" @default.
- W2993035901 modified "2023-09-26" @default.
- W2993035901 title "Comparison of Meta-heuristic Algorithms on Benchmark Functions" @default.
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- W2993035901 doi "https://doi.org/10.33793/acperpro.02.03.41" @default.
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