A Variable Search Space Strategy Based on Sequential Trust Region Determination Technique.

Area of Science:

• Optimization algorithms
• Computational intelligence
• Metaheuristic optimization

Background:

• Optimization problem complexity is defined by decision and objective spaces.
• Research has primarily focused on objective space improvements for metaheuristics, neglecting the decision space.
• Metaheuristic algorithms may struggle to find optimal solutions within the entire feasible region.

Purpose of the Study:

• To propose a variable search space (VSS) strategy to enhance metaheuristic algorithm performance.
• To address the challenge of suboptimal results in large feasible regions.
• To investigate the effectiveness of reducing the search space by finding a trust region.

Main Methods:

• A variable search space (VSS) strategy based on sequential trust region determination is introduced.
• The VSS divides the optimization into two stages: trust region identification and solution search within the region.
• The approach was evaluated on IEEE CEC2014 and BBOB2012 test suites.

Main Results:

• The VSS strategy was effective in improving the performance of metaheuristic algorithms.
• Locating a trust region significantly enhanced solution precision, particularly for complex problems.
• The sequential determination of trust domains for each variable proved beneficial.

Conclusions:

• Reducing the search space through a trust region is a viable method to improve metaheuristic algorithm convergence and precision.
• The proposed VSS strategy offers a promising approach for tackling complex optimization challenges.
• Further research into trust region determination can lead to more robust optimization techniques.