A Novel Evolutionary Algorithm: One-Dimensional Subspaces Optimization Algorithm (1D-SOA)

• Published in: Symmetry (Basel) Vol. 15; no. 10; p. 1873
• Main Authors: Díaz-Cortés, Gabriela Berenice, Luna-García, René
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Algorithms; Evolutionary algorithms; Exploitation; Genetic algorithms; Mathematical optimization; Optimization; Optimization algorithms; Optimization techniques; Subspaces; Variables; Germany
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym15101873

This paper introduces an evolutionary algorithm for n-dimensional single objective optimization problems: One-Dimensional Subspaces Optimization Algorithm (1D-SOA). The algorithm starts with an initial population in randomly selected positions. For each individual, a percentage of the total number of dimensions is selected, each dimension corresponding to a one-dimensional subspace. Later, it performs a symmetric search for the nearest local optima in all the selected one-dimensional subspaces (1D-S), for each individual at a time. The search stops if the new position does not improve the value of the objective function over all the selected 1D-S. The performance of the algorithm was compared against 11 algorithms and tested with 30 benchmark functions in 2 dimensions (D) and 30D. The proposed algorithm showed a better performance than all other studied algorithms for large dimensions.