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2. A novel metaheuristic for solving LSGO problems.
- Author
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Vakhnin, Aleksei and Sopov, Evgenii
- Subjects
- *
METAHEURISTIC algorithms , *EVOLUTIONARY algorithms , *PROBLEM solving , *GLOBAL optimization , *COEVOLUTION , *ALGORITHMS - Abstract
Evolutionary algorithms show outstanding performance when they are applied to optimization problems with a few variables, i.e. problems with less than a hundred continuous variables. Large-scale global optimization with continuous variables is still a challenging task for a wide range of evolutionary algorithms. Their performance decreases when the number of variables increases because the search space grows exponentially. Classic evolutionary algorithms cannot find a good solution using the allocated resources. A cooperative coevolution approach is a good tool for increasing the performance of an optimizer in solving high-dimensional problems. The approach splits the objective vector into a predefined number of parts (subcomponents), and each part is optimized by its optimizer. This paper makes an effort to solve the problem of selecting the number of subcomponents. The paper represents a novel metaheuristic for solving optimization problems with a huge number of continuous variables. The suggested approach is based on the self-adaptive cooperation of algorithms and the cooperative coevolution approach. Each algorithm has a unique number of subcomponents. The metaheuristic automatically allocates resources between algorithms during the optimization process. Algorithms optimize the same population one by one. The proposed metaheuristic is titled COSACC, coordination of self-adaptive cooperative coevolution algorithms. We have evaluated the proposed algorithm on fifteen problems from the IEEE LSGO CEC'2013 benchmark. The study demonstrates that COSACC outperforms in average cooperative coevolution algorithms with the static number of subcomponents. Wilcoxon test has proven the results of numerical experiments. We have tested COSACC performance with other state-of-the-art metaheuristics, COSACC is a competitive approach. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Vector Direction of Filled Function Method on Solving Unconstrained Global Optimization Problem.
- Author
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Napitupulu, Herlina and Mohd, Ismail Bin
- Subjects
- *
ALGORITHMS , *PARAMETERS (Statistics) , *PROBLEM solving , *VECTOR analysis , *SADDLEPOINT approximations , *GLOBAL optimization - Abstract
Filled function method is one of deterministic methods for solving global minimization problems. Filled function algorithm method generally contains of two main phases. First phase is to obtain local minimizer of objective function, second is to obtain minimizer or saddle point of filled function. In the second phase, vector direction plays an important role on finding stationary point of filled function, by assist in escaping from neighborhood of current minimizer of objective function of the first phase. In this paper, we introduce parameter free filled function and some typical vector direction to be applied in filled function algorithm. The algorithm method is implemented into some benchmark test functions. General computational and numerical results are presented to show the performance of each vector direction on filled function method for solving two dimensional unconstrained global optimization problems. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
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