Initialization and Diversity in Optimization Algorithms [Hardcover]

1. Introduction to Swarm Optimization. 2. Two Classical Metaheuristics: Differential Evolution and Particle Swarm Optimization. 3. The Influence of Initialization in Metaheuristics. 4. Different Methodologies for Initialization. 5. Implementation of Initialization Methods in PSO and DE. 6. The Importance of Diversity in Metaheuristics. 7. Different Indicators for Measuring Diversity. 8. Implementation of Diversity Indicators in DE and PSO. 8. Implementation of Diversity Indicators in DE and PSO. 9. Pros and Cons of the Use of Different Initializations and Diversity Indicators. Appendix A. Test Functions. Appendix B. MATLAB codes for Initialization Methods and 2D Visualization. Appendix C. Solutions.

Designing new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.

Maintaining diversity among the population elements throughout the iterative process is also essential. This diversity facilitates a more thorough and efficient exploration of the search space. In swarm intelligence algorithms, there are multiple methods to measure diversity, each with its own advantages and disadvantages.

This book presents the theory behind the initialization process and the different mechanisms. Additionally, it includes a comparative study of various diversity indicators. It explores different methodologies to compute its value and explains howl3ƒ