A Brief Review of Nature-Inspired Algorithms for Optimization

Abstract

Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very efficient and thus have become popular tools for solving real-world problems. Some algorithms are insufficiently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.

References in corpus (3)

• Novel type of phase transition in a system of self-driven particles
• A comprehensive review of firefly algorithms
• Multi-objective Flower Algorithm for Optimization

Cited by in corpus (10)

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• Drone Squadron Optimization: a Self-adaptive Algorithm for Global Numerical Optimization
• Analysis of Microarray Data using Artificial Intelligence Based Techniques
• Performance-aware placement and chaining scheme for virtualized network functions: a particle swarm optimization approach
• Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem
• The Role of Evolution in Machine Intelligence
• Negotiating Team Formation Using Deep Reinforcement Learning
• Mapping of Real World Problems to Nature Inspired Algorithm using Goal based Classification and TRIZ