Dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Sand Cat Swarm Optimization (SCSO) algorithm faces challenges with limited population diversity, low efficiency on complex functions, and a tendency to get stuck in local optima.
• Effective optimization algorithms are crucial for solving complex computational problems in various scientific and engineering fields.

Purpose of the Study:

• To propose an enhanced optimization algorithm, the Dual-Path Differential Perturbation Sand Cat Swarm Optimization (EDSCSO), to address the limitations of the original SCSO.
• To improve population diversity, convergence speed, and the ability to escape local optima in optimization tasks.

Main Methods:

• Introduction of an escape mechanism to balance exploration and exploitation.
• Implementation of a random elite cooperative guidance strategy to accelerate convergence.
• Application of a dual-path differential perturbation strategy with variational operators to enhance population diversity.

Main Results:

• EDSCSO achieved superior average fitness on 27 out of 39 test functions from IEEE CEC2017 and IEEE CEC2019 benchmark suites.
• The algorithm demonstrated efficiency and feasibility for complex optimization problems.
• Optimal solutions were found for 3D wireless sensor network coverage and UAV path planning problems.

Conclusions:

• The proposed EDSCSO algorithm offers a significant improvement over the original SCSO, effectively addressing its limitations.
• EDSCSO proves to be a capable and efficient tool for tackling complex optimization challenges.
• The algorithm's successful application in real-world scenarios validates its practical applicability.