Bird swarm algoritms with chaotic mapping

Bird Swarm Algoritms with Chaotic Mapping Optimization known as also mathematical programming, is a collection of processes that select the most appropriate values of decision variables according to a goal (evaluation) function. Many algorithms have been proposed for optimization problems. Most of these algorithms need mathematical models for model of system and objective function. General purposed heuristic optimization algorithms are used in order to obtain the solution in reasonable time when mathematical models cannot be derived. General purposed heuristic optimization algorithms are evaluated in eight different groups including biology-based, physics-based, swarm-based, social-based, music-based, chemistry-based, sports based, and mathematics based. Swarm intelligence based optimization algorithms have been developed by observing the movements of live swarms such as bird, fish, cat, and bee. In order to increase the fast convergence and high accuracy of the optimization algorithms, chaotic maps have been used in many algorithms. Bird Swarm Algorithm (BSA) is one of the most recent swarm based algorithms. This is the first time chaos has been introduced to increase the global convergence feature and prevent from being stuck in the local solution of BSA. In this thesis, BSA and chaotic BSA were studied in detail and the performances of the algorithms have been tested on unimodal and multi modal benchmark functions with different dimensions and three constrained real-life problem. In these investigations, tendency of converging to optimum is used as a measure of performance. Experimental results have been presented and interpreted through comparative tables and graphs. It is expected that this algorithm will be efficiently used in many different types of complex problems due to high performance of the algorithm in both unimodal and multi modal functions.