Bio-inspired algorithms harnessing cricket chirping behaviour for single and multi- objective optimization

Abstract

Optimization techniques find their application in almost every field of concern. The task of optimization is obtaining the maxima or minima, subject to the various constraints specified. The problems can be single-objective or multi-objective and correspondingly the techniques can be categorized as a single objective optimization technique and multi-objective optimization techniques. Some techniques aim at finding only the optimal solution and are termed as exact methods. They take exponential time to achieve their motive. An alternative to this is the approximate methods that attempt to determine a near-optimal solution in a reasonable amount of time. The majority of the approximate approaches derives their concepts from Biology and Mother Nature. Evolutionary and swarm-based algorithms can be quoted as typical examples for this case. Though there are numerous optimization techniques, there also exist certain barriers that hurdle in attaining the maximum efficiency. They include the premature convergence, rigorous parameter tuning, non- generalization, high computational cost and difficult implementation. Further, the No Free Lunch Theorem, which states that all algorithms perform similarly when averaged on all functions and designing an algorithm to suit all applications will result in vain, encourages the formulation of new optimization algorithms. In this research, new optimization techniques to overcome the stated barriers have been formulated. The chirping behaviour and movement of the insect named cricket have placed the primary emphasis while devising the algorithm. Initially, a cricket chirping algorithm (CCA) for single-objective optimization is designed. In this algorithm, the unique chirping nature of male crickets while mating and aggression are exploited. The male crickets chirp with an exclusive sound to attract the females for mating and simultaneously repel the males. Another kind of chirping sound is emitted inspired algorithms for single and multi-objective optimization. The single objective CCA is tested on many test functions. The parameters are fine-tuned based on an exhaustive analysis of the algorithm. during aggression when another male cricket nears it with the intent to fight. In the case of aggression, the winner survives and takes the position of the loser and the loser is discarded. This behaviour of cricket has been harnessed for the design and development of the bio-inspired algorithms for single and multi-objective optimization.