Kiran, Mustafa ServetGunduz, MesutBaykan, Omer Kaan2020-03-262020-03-2620120096-30031873-5649https://dx.doi.org/10.1016/j.amc.2012.06.078https://hdl.handle.net/20.500.12395/27685This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) and called hybrid ant particle optimization algorithm (HAP) to find global minimum. In the proposed method, ACO and PSO work separately at each iteration and produce their solutions. The best solution is selected as the global best of the system and its parameters are used to select the new position of particles and ants at the next iteration. The performance of proposed method is compared with PSO and ACO on the benchmark problems and better quality results are obtained by HAP algorithm. (C) 2012 Elsevier Inc. All rights reserved.en10.1016/j.amc.2012.06.078info:eu-repo/semantics/closedAccessParticle swarm optimizationAnt colony optimizationHybrid metaheuristicGlobal minimumArticle219415151521Q1WOS:000310504000010Q1