A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems

Abstract

Hybridization of metaheuristic algorithms with local search has been investigated in many studies. This paper proposes a hybrid pathfinder algorithm (HPFA), which incorporates the mutation operator in differential evolution (DE) into the pathfinder algorithm (PFA). The proposed algorithm combines the searching ability of both PFA and DE. With a test on a set of twenty-four unconstrained benchmark functions including both unimodal continuous functions, multimodal continuous functions, and composition functions, HPFA is proved to have significant improvement over the pathfinder algorithm and the other comparison algorithms. Then HPFA is used for data clustering, constrained problems, and engineering design problems. The experimental results show that the proposed HPFA got better results than the other comparison algorithms and is a competitive approach for solving partitioning clustering, constrained problems, and engineering design problems.

An Improved Pathfinder Algorithm (ASDR-PFA) based on Adaptation of Search Dimensional Ratio for solving constrained optimization problems and optimal Feature Selection

“…𝑃(𝑋 𝑖 ) = ∑ 𝛼 𝑗 × max (0, 𝑔 𝑗 (𝑋 𝑖 )) + ∑ 𝛽 𝑘 × max(0, |ℎ 𝑘 (𝑋 𝑖 )| − 𝛿) 𝑚 ℎ 𝑘=1 𝑚 𝑔 𝑗=1 (10) In Eq. 10, max(0, 𝑔 𝑗 (𝑋 𝑖 )) and max(0, |ℎ 𝑘 (𝑋 𝑖 )| − 𝛿) represent the worth of violations by 𝑋 𝑖 for the 𝑗 𝑡ℎ inequality and 𝑘 𝑡ℎ equality constraints respectively, 𝛼 𝑗 and 𝛽 𝑘 signify the penalty effects for these constraints that affect the quality of an answer.…”

“…Qi et al introduced a hybrid PFA (HPFA) by the incorporation of the mutation operator in differential evolution (DE) along with the combined searchability of both PFA and DE. To evaluate the PFA, they have applied it to a set of benchmark functions, data clustering, and some constrained engineering design problems [10]. Authors Dong et al suggested HPFA to resolve the green flow shop scheduling problem with limited buffers and energy threshold constraints (GFSSP_LBET).…”

Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules

“…And there have been many research studies on the combination of metaheuristic algorithms and local search. e mutation operator in differential evolution is introduced into PFA (HPFA) [26]. A new classification for the source of inspiration for nature-inspired algorithms was designed too, and it can be classified into four groups: evolutionary techniques, swarm intelligence techniques, physics-based techniques, and human-related techniques.…”

Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization

“…In recent decades, one of the main challenges of artificial intelligence experts has been the applicability of the proposed metaheuristic algorithm in different fields to optimize and improve the overall efficiency of some specific problems. Artificial electric field algorithm for engineering design optimization 49 , black widow algorithm for engineering optimization 47 , earthquake engineering optimization of structures 50 , engineering design optimization with queuing search algorithm 25 , cuckoo search algorithm for optimization of the travelling salesman problem 51 , optimum design of engineering problems with sine cosine grey wolf optimizer 52 , engineering design optimization with self-adaptive Rao algorithm 53 , design optimization of numerical and engineering optimization problems with improved Harris Hawks optimizer 54 , unconstrained and constrained optimization by hybrid pathfinder optimizer 55 , improved charged system search for optimization of fuzzy controllers 56 , optimum design of structural systems with metaheuristics 57,58 are some of the most recent research works in the area of applied artificial intelligence.…”