AHPS2: An optimizer using adaptive heterogeneous particle swarms

A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization

“…The performance of the proposed method is compared with several recently proposed methods, such as the multilevel cooperative coevolution (ML-CC) [48], cooperative coevolution with delta grouping (CC-DG) [49], differential evolution with cooperative coevolution with several variants (DECC-G, DECC-DML, DECC-D, DECC-VP) [49], [50], sequential DE enhanced by neighborhood search (SDEeNS) [51], cooperative PSO (CPSO-H) [10], DMSPSO [26], [52], and AHPS 2 [53]. The default values used for parameter settings for all of the methods are described in [53], and the maximum fitness evaluations of FEs max are set at 3.00E+06, which is the stopping criterion. The population size S used by these methods is set to S = 50.…”

Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems

“…This novel strategy updates a given particle's velocity using all other particles' personal best information. Further refinements include the usage of personal best of neighbouring particles [47,31], distance and fitness information [51,61], multi-layer swarms [36,62] and multi-swarm strategies [7,69]. The refinements and modifications of the PSO algorithm using different learning strategies and hybridization have successfully provided better solutions to the complex optimization problems.…”

Advances in Swarm Intelligence

“…In addition, [11] reviewed the state-of-the-art papers on facility layout problem with quadratic assignment model and mixed integer-programming models. In particular: ant colony optimization in [16][17][18][19]; evolution strategies [20], genetic algorithms [21][22][23][24][25][26]; greedy randomized adaptive search procedures [27]; hybrid heuristics [28][29][30] Due to the hardness of the QAP for heuristic methods [37], this problem is considered suitable as a testing platform for innovative intelligent optimization techniques or improvement methods like metaheuristics [38]. However, due to the intrinsic complexity in Facility Layout Problem, which are of the NP-hard type -like we previously said-the attention of the researchers is focused on the development of heuristics and metaheuristics for solving this problem with the less computational effort.…”

“…To comprehensively test the performance of HLABC, we chose the functions with different features, such as unimodal, multimodal, shifted, rotated [29]. The purpose of shifting benchmark functions is to shift the optimal solution from its original position.…”