A novel local search method for LSGO with golden ratio and dynamic search step
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METHODOLOGIES AND APPLICATION
A novel local search method for LSGO with golden ratio and dynamic search step Havva Gu¨l Koçer1 • Sait Ali Uymaz2
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Depending on the developing technology, large-scale problems have emerged in many areas such as business, science, and engineering. Therefore, large-scale optimization problems and solution techniques have become an important research field. One of the most effective methods used in this research field is memetic algorithm which is the combination of evolutionary algorithms and local search methods. The local search method is an important part that greatly affects the memetic algorithm’s performance. In this paper, a novel local search method which can be used in memetic algorithms is proposed. This local search method is named as golden ratio guided local search with dynamic step size (GRGLS). To evaluate the performance of proposed local search method, two different performance evaluations were performed. In the first evaluation, memetic success history-based adaptive differential evolution with linear population size reduction and semi-parameter adaptation (MLSHADE-SPA) was chosen as the main framework and comparison is made between three local search methods which are GRGLS, multiple trajectory search local search (MTS-LS1) and modified multiple trajectory search. In the second evaluation, the improved MLSHADE-SPA (IMLSHADE-SPA) framework which is a combination of MLSHADE-SPA framework and proposed local search method (GRGLS) was compared with some recently proposed nine algorithms. Both of the experiments were performed using CEC’2013 benchmark set designed for large-scale global optimization. In general terms, the proposed method achieves good results in all functions, but it performs superior on overlapping and non-separable functions. Keywords Large-scale global optimization Local search Golden ratio Memetic algorithm CEC’2013 LSGO benchmark
1 Introduction Optimization is a problem-solving technique and its purpose is to calculate the parameters that will produce the optimum result of a function. In other words, the goal in optimization is to find a global optimum for an objective function defined on a specific search space and under some constraints. Most problems in the real world such as scheduling, vehicle routing, bio-computing and
Communicated by V. Loia. & Sait Ali Uymaz [email protected] 1
Distance Education Application and Research Center, Selcuk University, Konya, Turkey
2
Department of Computer Engineering, Konya Technical University, Konya, Turkey
engineering problems, etc., are concerned with finding the maximum or minimum value of a function efficiently. So such real-world problems can be called optimization problems. Large number of decision variables included optimization problems are known as large-scale optimization problems. Large-scale global optimization (LSGO) term is used for this particular category of global o
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