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A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy.

Meilan Yang¹, Yanmin Liu², Jie Yang²

¹School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China.

Computational Intelligence and Neuroscience

|March 21, 2022

Summary

This summary is machine-generated.

This study introduces a novel hybrid multi-objective particle swarm optimization (CCHMOPSO) algorithm. CCHMOPSO enhances solutions by using disturbance and central control strategies, outperforming existing methods for complex optimization tasks.

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Area of Science:

Computational Intelligence
Optimization Algorithms
Swarm Intelligence

Background:

Multi-objective optimization problems (MOPs) are prevalent in various scientific and engineering fields.
Existing multi-objective particle swarm optimization (MOPSO) algorithms face challenges like local extrema and suboptimal solution distribution.
Improvements to MOPSO are crucial for effectively solving complex MOPs.

Purpose of the Study:

To propose a novel hybrid multi-objective particle swarm optimization algorithm named CCHMOPSO.
To enhance the performance of MOPSO by incorporating a central control strategy and disturbance mechanisms.
To improve the diversity and distribution of solutions for MOPs.

Main Methods:

Developed a hybrid MOPSO (CCHMOPSO) incorporating a disturbance strategy based on boundary fluctuations.
Implemented a central control strategy for updating the external archive when capacity is reached.
Introduced a combination method to update the individual best particle to enhance population diversity when dominance is unclear.

Main Results:

CCHMOPSO demonstrated superior performance compared to four other MOPSO algorithms.
The proposed algorithm also outperformed four multi-objective evolutionary algorithms in experimental tests.
The disturbance and central control strategies effectively prevented local extrema and improved solution distribution.

Conclusions:

CCHMOPSO is a feasible and effective method for solving multi-objective optimization problems.
The hybrid approach offers significant advantages over existing MOPSO and evolutionary algorithms.
The central control and disturbance strategies are key innovations for improving MOPSO performance.