A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

Area of Science:

• Computer Science
• Artificial Intelligence
• Optimization Algorithms

Background:

• Global optimization problems are prevalent in various scientific and engineering fields.
• Existing algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bat Algorithm (BA) have limitations.
• There is a need for robust algorithms that balance exploration and exploitation effectively.

Purpose of the Study:

• To propose a novel Guiding Evolutionary Algorithm (GEA) for global optimization.
• To enhance the performance of evolutionary algorithms by incorporating a greedy strategy and global best guidance.
• To address the disadvantages of traditional evolutionary and swarm intelligence algorithms.

Main Methods:

• GEA combines principles from GA, PSO, and BA.
• Individuals are crossed with the global best, guiding offspring towards promising regions.
• Dynamic mutation and local search probabilities are employed to balance exploration and exploitation.
• A greedy strategy is integrated for enhanced convergence.

Main Results:

• GEA demonstrated superior performance compared to three other typical global optimization algorithms.
• The guided approach effectively attracted offspring to optimal regions in the genotype space.
• Dynamic probabilities for mutation and local search contributed to improved exploitation capabilities.

Conclusions:

• The proposed Guiding Evolutionary Algorithm (GEA) offers a promising approach for global optimization.
• GEA effectively balances exploration and exploitation, leading to better solution quality.
• The algorithm's design successfully integrates beneficial aspects of multiple optimization techniques.