A Critical Review on Nature Inspired Optimization Algorithms

Abstract

Nature-inspired optimization algorithms turned out to be progressively more accepted in modern era, and the majority of these meta-heuristic algorithms, such as ‘Bat algorithm’, ‘Lion Algorithm’, ‘Particle swarm optimization’, ‘Water wave optimization algorithm’, ‘Elephant herding optimization algorithm’, ‘Optics inspired optimization algorithm’, ‘Cuckoo search’, ‘Flower algorithms’, ‘Genetic algorithms’, ‘Differential evolution’, ‘Harmony search’, ‘Simulated annealing’, and many more. This paper attempts to establish the most recent improvements concerning all main ‘Nature-Inspired Algorithms’ and it is strived to coat at least one hottest optimization problem related to these above-mentioned algorithms. Based on their outcomes it is established that there are a few considerable gaps between hypothesis and observe. On one side, nature-inspired algorithms for optimization are extremely successful and can acquire most favourable clarifications in a convincingly realistic time. On the other side, mathematical investigation of key characteristics of these algorithms is missing. It is extremely advantageous that researchers add on some impending into the neutrality of diverse nature-inspired algorithms and hence can obtain the dare to crack key difficulties that must be solved. The paper is extending extensively and intensifying the need to evaluate and review these various optimization algorithms. This paper is organized in a manner to provide complete description of each NIA starting from introduction then review of literature, analysis of various NIAs and finally conclusion.