Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems.

Area of Science:

• Optimization Algorithms
• Computational Intelligence
• Nature-Inspired Computing

Background:

• The Salp Swarm Algorithm (SSA) is a nature-inspired optimization algorithm known for its simple framework.
• However, SSA faces challenges with convergence speed and local optima in complex, multimodal, and high-dimensional optimization problems.

Purpose of the Study:

• To address the limitations of the standard SSA.
• To enhance the convergence speed and accuracy of SSA for complex optimization tasks.

Main Methods:

• Introduction of chaotic initialization to generate a superior initial population for better global optimum searching.
• Integration of differential evolution to augment the search capabilities and balance global exploration with local exploitation in SSA.

Main Results:

• The Chaotic SSA with Differential Evolution (CDESSA) demonstrated accelerated convergence.
• Experimental validation on IEEE CEC2014 benchmark functions, feature selection problems, and constrained engineering optimization problems showed significant performance improvements.
• CDESSA outperformed the original SSA and other comparative methods.

Conclusions:

• CDESSA effectively enhances SSA's performance in tackling complex optimization problems.
• The integration of chaotic initialization and differential evolution provides a robust framework for improved convergence and accuracy.