Self-adaptive dual-strategy differential evolution algorithm.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Evolutionary Computation

Background:

• Differential evolution (DE) algorithms face a trade-off between exploration and exploitation.
• Balancing these contradictory search behaviors is crucial for effective optimization.

Purpose of the Study:

• To propose a novel self-adaptive dual-strategy differential evolution (SaDSDE) algorithm.
• To improve the balance between exploration and exploitation in DE algorithms.
• To enhance global optimization performance.

Main Methods:

• Introduced a dual-strategy mutation operator combining "DE/best/2" (global exploration) and "DE/rand/2" (local exploitation).
• Implemented an individual-dependent and fitness-dependent self-adaptive scaling factor strategy.
• Incorporated an exploration ability control factor for dynamic adjustment of global exploration.

Main Results:

• SaDSDE demonstrated remarkable improvements in global optimization performance.
• The algorithm was compared against 7 DE variants and 3 non-DE algorithms on 30 Benchmark test functions.
• Performance superiority of SaDSDE increased with problem dimensionality (30 and 100 dimensions).

Conclusions:

• The proposed SaDSDE algorithm effectively balances exploration and exploitation.
• SaDSDE offers superior global optimization capabilities, particularly for high-dimensional problems.
• The self-adaptive strategies contribute to improved and robust performance.