Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Evolutionary Computation

Background:

• Multi-objective optimization problems (MOPs) are prevalent in science and engineering.
• Existing methods like Strength Pareto Evolutionary Algorithm 2 (SPEA2) and competitive multi-objective PSO (MO-PSO) have limitations.
• Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique.

Purpose of the Study:

• To propose an efficient Particle Swarm Optimization (PSO) technique for multi-objective optimization problems.
• To develop hybrid algorithms integrating PSO with evolutionary algorithms (EA) based on the Strength Pareto approach.
• To evaluate the performance of the proposed algorithms against established benchmarks and methods.

Main Methods:

• Developed a modified PSO algorithm incorporating the Strength Pareto approach.
• Created three hybrid EA-PSO algorithms for solving MOPs.
• Tested algorithms on seven benchmark problems.
• Compared results with SPEA2 and MO-PSO using multiple performance metrics.

Main Results:

• The proposed Strength Pareto PSO exhibits slower convergence but requires less computational time.
• Hybrid EA-PSO algorithms demonstrated superior performance compared to SPEA2, MO-PSO, and the standalone Strength Pareto PSO.
• Hybrid approaches outperformed existing methods across various performance metrics.

Conclusions:

• Combining PSO and evolutionary algorithms yields highly effective hybrid optimization techniques.
• The proposed Strength Pareto PSO and its hybrid forms offer a competitive alternative for solving MOPs.
• These findings advance the field of multi-objective optimization through novel algorithmic integration.