Single candidate optimizer: a novel optimization algorithm

Abstract Single-solution-based optimization algorithms have gained little to no attention by the research community, unlike population-based approaches. This paper proposes a novel algorithm, called Single Candidate Optimizer (SCO), that relies only on single candidate solution throughout whole process. The proposed algorithm implements unique set of equations effectively update position solution. To balance exploration and exploitation, SCO is integrated with two-phase strategy where updates its differently in each phase. effectiveness approach validated testing it thirty three classical benchmarking functions four real-world engineering problems. compared well-known algorithms, i.e., Particle Swarm Optimization, Grey Wolf Optimizer, Gravitational Search Algorithm recent high-performance algorithms: Equilibrium Archimedes Optimization Algorithm, Mayfly Salp Algorithm. According Friedman Wilcoxon rank-sum tests, can significantly outperform all other for majority investigated results achieved motivates design development new single-solution-based further improve performance. source code publicly available at: https://uk.mathworks.com/matlabcentral/fileexchange/116100-single-candidate-optimizer .