A dynamic utopia point updating strategy for multi-objective optimization.

Area of Science:

• Engineering
• Computational Science
• Optimization Theory

Background:

• Multi-objective optimization problems are common in engineering.
• Existing Pareto front search methods suffer from high computational costs and local optimality.
• Efficiently solving these problems is crucial for practical applications.

Purpose of the Study:

• To develop a novel utopia optimization method for multi-objective problems.
• To reduce computational burden and overcome local optimality challenges.
• To enhance the optimization solution's computing ability.

Main Methods:

• Converted multi-objective problems into single-objective ones, avoiding repeated Pareto front searches.
• Implemented a dynamical utopia point updating strategy.
• Utilized an improved particle swarm optimization with Logistic map and adaptive weights.

Main Results:

• Reduced computational burden by converting problems to single-objective ones.
• Addressed local optimality through a dynamical utopia point strategy.
• Achieved relative error reductions of 1.45, 1.06, and 0.14 compared to existing methods.

Conclusions:

• The novel utopia optimization method is effective for multi-objective problems.
• The dynamical updating strategy and improved particle swarm optimization enhance performance.
• This approach offers significant improvements in accuracy and efficiency over traditional methods.