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A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems.

Xiangbo Qi¹, Zhonghu Yuan¹, Yan Song²

¹School of Mechanical Engineering, Shenyang University, Shenyang 110044, China.

Computational Intelligence and Neuroscience

|July 3, 2020

Summary

A new hybrid pathfinder algorithm (HPFA) combines Pathfinder Algorithm (PFA) and Differential Evolution (DE) for improved optimization. HPFA demonstrates superior performance in benchmark functions, data clustering, and engineering design problems.

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristic Computing

Background:

Metaheuristic algorithms combined with local search are widely studied for complex problem-solving.
Existing algorithms face challenges in balancing exploration and exploitation for diverse optimization tasks.

Purpose of the Study:

To introduce a novel hybrid pathfinder algorithm (HPFA) integrating Differential Evolution's mutation operator into the Pathfinder Algorithm.
To enhance the combined searching capabilities of the Pathfinder Algorithm and Differential Evolution.

Main Methods:

The hybrid pathfinder algorithm (HPFA) was developed by incorporating the mutation operator from Differential Evolution (DE) into the Pathfinder Algorithm (PFA).
HPFA was evaluated on twenty-four unconstrained benchmark functions, including unimodal, multimodal, and composition functions.
The algorithm's efficacy was further tested on data clustering, constrained optimization, and engineering design problems.

Main Results:

HPFA demonstrated significant improvements over the standard Pathfinder Algorithm and other comparative algorithms on benchmark functions.
Experimental results indicate HPFA achieved superior outcomes compared to other algorithms in data clustering, constrained problems, and engineering design.
The proposed HPFA exhibits competitive performance across various complex problem domains.

Conclusions:

The hybrid pathfinder algorithm (HPFA) effectively combines the strengths of PFA and DE for superior search capabilities.
HPFA presents a competitive and effective approach for solving partitioning clustering, constrained optimization, and engineering design challenges.
The integration of DE's mutation operator enhances the performance of the Pathfinder Algorithm in complex optimization scenarios.