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 Á Local search Á Golden ratio Á Memetic algorithm Á CEC'2013 LSGO benchmark Communicated by V. Loia.
Optimization technology is used to accelerate decision-making processes and to increase the quality of decision making in management and engineering problems. The development technology has made real world problems large and complex. Many optimization methods that proposed for solving large-scale global optimization (LSGO) problems suffer from the "curse of dimensionality", which implies that their performance deteriorates quickly as the dimensionality of the search space increases. Therefore, more efficient and robust algorithms are needed. When literature on large-scale optimization problems is examined, it is seen that algorithms with effective global search ability have better results. For the purpose, in this paper Modified Artificial Algae Algorithm (MAAA) is proposed by modifying original version of Artificial Algae Algorithm (AAA) inspiring by Differential Evolution Algorithm (DE)'s mutation strategies. AAA and MAAA are compared with each other by operating with the first 10 benchmark functions of CEC2010 Special Session on Large Scale Global Optimization. The results show that hybridization process that applied by updating an additional fourth dimension with mutation strategies of DE after the helical motion of the AAA algorithm, contributes exploration phase and improves the AAA performance on LSGO.
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