Nature-Inspired Algorithms in Optimization: Introduction, Hybridization and Insights

TL;DR

This chapter addresses the challenge of solving complex optimization problems by leveraging nature-inspired metaheuristics and their hybrids. It presents the fundamental components of optimization, contrasts gradient-based and gradient-free approaches, and surveys core nature-inspired algorithms (PSO, BA, FA, CS, FPA) along with their mechanisms and limitations. It then outlines four hybridization schemes (sequential, parallel, full, mixed), discusses the pitfalls of random component mixing, and provides practical insights and recommendations for designing effective hybrids, tuning parameters, and benchmarking. The work offers a structured guide for researchers to select, combine, and evaluate optimization algorithms, with emphasis on synergy, clear structure, and simplicity to advance performance on real-world problems.

Abstract

Many problems in science and engineering are optimization problems, which may require sophisticated optimization techniques to solve. Nature-inspired algorithms are a class of metaheuristic algorithms for optimization, and some algorithms or variants are often developed by hybridization. Benchmarking is also important in evaluating the performance of optimization algorithms. This chapter focuses on the overview of optimization, nature-inspired algorithms and the role of hybridization. We will also highlight some issues with hybridization of algorithms.

Figures (4)

• Figure 1: Important components of optimization.
• Figure 2: Steps to create a new hybrid algorithm.
• Figure 3: Sequential structure of hybridization.
• Figure 4: Parallel structure of hybridization.