Karmaşık optimizasyon problemlerinin çözümü için metasezgisel algoritmaların paralel hesaplama yoluyla koalisyonu

Most of the real-life problems could be modeled as optimization problems and the need for effective solution of these problems is always in demand. In this context, efforts to develop effective approaches to solving optimization problems have been the subject of considerable research. These approaches usually combine general-purpose or particular rules in a logical scope and the methods that are formed by the logical combination of these rules are called optimization algorithms. In this study several metaheuristic algorithms are brought together to form a coalition under Weighted Superposition Attraction-Repulsion Algorithm (WSAR) in a parallel computing environment for solving complex optimization problems. The proposed approach runs different single solution based metaheuristic algorithms (SSBMAs) in parallel and employs WSAR (which a recently developed recently proposed swarm intelligence based optimizer) as controller. While SSBMAs are responsible for exploring the search space, WSAR controls the communication process between the SSBMAs. The presented method tested against some well-known complex optimization problems in three groups, namely, continuous optimization problems, binary optimization problems and combinatorial optimization problems. While CEC 2020 problems are selected as test problems for continuous optimization problems, the uncapacitated facility location problem (UFLP) and the set union knapsack problem (SUKP) are selected as test case for binary optimization problems. In addition, the Resource Constrained Project Scheduling Problem (RCPSP) and the Permutation Flow Shop Scheduling Problem (PFSP) are selected as test problems for combinatorial optimization. The obtained results are compared with some other optimization algorithms. The results of the comparison show that the proposed approach is competitive in terms of solution quality and solution time.