A novel local search method for LSGO with golden ratio and dynamic search step

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 Á Local search Á Golden ratio Á Memetic algorithm Á CEC'2013 LSGO benchmark Communicated by V. Loia.

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

“…In DSRegPSO, a new updating speed equation is defined for each iteration (k) in Equation (11), taking into account the hyper-sphere diameter and its expansion speed. The inertial momentum component varies based on these factors.…”

“…The IEEE Congress on Evolutionary Computation (CEC) tool is a widely used benchmark for LSGO problems. It includes optimization functions that simulate real-world problems and has been used to evaluate algorithms in [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Several metaheuristics have been applied to address the challenges associated with solving Large-Scale Global Optimization (LSGO) problems.…”

A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

“…An improved MLSHADE-SPA (IMLSHADE-SPA) framework was proposed in [ 11 ], which replaced the local search method (MMTS) with a new local search method and achieved higher performance.…”

“…MPCE & SSALS was compared with other state-of-the-art algorithms, including SHADE-ILS [ 12 ], MLSHADE-SPA [ 5 ], CBCC-RDG3 [ 17 ], and IMLSHADE-SPA [ 11 ]. Among them, SHADE-ILS, CBCC-RDG3, and MLSHADE-SPA were currently the top three algorithms in the CEC LSGO competitions, and IMLSHADE-SPA is an improved version of MLSHADE-SPA.…”

“…MLSHADE-SPA [ 5 ] also is a hybrid algorithm that is based on three Differential Evolution (DE) strategies and a modified Multiple Trajectory Search (MTS) [ 10 ] algorithm. IMLSHADE-SPA [ 11 ] is an improved MLSHADE-SPA with a novel local search method. However, SHADE-ILS [ 12 ] is an enhanced version of the SHADE algorithm.…”