Nature-Inspired Algorithms: A Comprehensive Review
This chapter focuses the light on new research trends. It discusses classifications of the nature-inspired algorithms and presents a brief list of algorithms followed by several different variants nature-inspired algorithms. To a certain extent, new optimization algorithms are developed and modified based on the nature as a main source of inspiration. Sometimes nature-inspired algorithms produce inaccurate solutions for some real-time optimization problems. Imitating the best feature in nature to solve optimization problems is an ongoing work. The chapter discusses the most nature-inspired algorithms clarifying their main inspiration source, motivation scenario, mathematical model, and heuristics. Algorithms could be categorized into eight main categories depending on problem type: continuous, discrete, parallel, distributed, binary, chaotic, multi-objective and hybrid algorithms. Instead, optimization algorithms are classified according to a high level set of selected criteria like main inspiration sources, e.g., biology, physics or chemistry. To a certain extent, new optimization algorithms are developed and modified based on the nature as a main source of inspiration.