Accelerated opposition learning based chaotic single candidate optimization algorithm: A new alternative to population-based heuristics

This study considers the Single Candidate Optimizer (SCO) as an alternative to population-based heuristics, that is faster than them. Although the SCO algorithm is a fast single-candidate-based heuristic, it has certain limitations. To overcome these limitations and enhance the search performance of SCO, several solutions were proposed in this study. First, owing to the single-candidate nature of the SCO, the initial solution position can play a critical role. To compensate for this, an accelerated opposition-learning mechanism was integrated into the SCO. In addition, instead of the equation that is active when the number of unsuccessful improvement attempts is reached in the SCO structure, a mutation operator including chaotic functions (Levy, Gauss, and Cauchy) has been incorporated into the algorithm. Again, equations based on new approaches were added to the SCO algorithm to update the position of the candidate solution during the exploration and exploitation phases. Finally, the standard boundary value control mechanism is replaced with a more effective one. The algorithm developed in this study is named Accelerated Opposition Learning based Chaotic Single Candidate Optimizer (AccOppCSCO), inspired by the accelerated opposition learning mechanism and the mutation operator involving chaotic behaviors. The search capability of the proposed AccOppCSCO algorithm was first analyzed using four different methods: convergence, search history, trajectory, and computational complexity. The effectiveness of the mechanisms used in the AccOppCSCO algorithm for four different two-dimensional benchmark problems from the IEEE Congress on Evolutionary Computation 2014 (CEC2014) package was demonstrated. Subsequently, the performance of the proposed AccOppCSCO algorithm was evaluated on the CEC2014 and IEEE Congress on Evolutionary Computation 2020 (CEC2020) benchmark problems with different dimensions. The results show that the AccOppCSCO algorithm works effectively in the CEC2014 and CEC2020 test sets and offers better optimization results than SCO. The AccOppCSCO algorithm ranked first in the overall evaluation of the 30-dimensional CEC2014 comparison results with State of the Art (SOTA) heuristics from the literature. Finally, for ten different engineering design problems, the AccOppCSCO algorithm was analyzed and compared with the original SCO and other SOTA heuristics. The results show that AccOppCSCO is effective for engineering design problems. This emphasizes that the algorithm can work effectively on a wide range of problems and can be used in various applications. The source code of the AccOppCSCO algorithm for the CEC2014 benchmark suite is publicly available at https://github.com/uguryuzgec/AccOppCSCO.