Drosophila Food-Search Optimization
The method of finding optimal solution to an optimization problem is a recent challenge for the researchers. In order to solve an optimization problem many evolutionary methods have been introduced as alternate paradigms. In this paper an extensive efforts has been made to solve unconstrained optimization problems by proposing a new algorithm namely Drosophila Food-Search Optimization (DFO) Algorithm. DFO mimics the food-search mechanism of a fly in nature based on called Drosophila Melanogaster. To maintain the diversity throughout the population during simulation, an exploration operation has been developed to generate new individuals. A set of well known benchmark function have been used to validate the better performance of DFO. The experimental results confirms that the proposed technique DFO performs better than some well known existing algorithms like Differential Evolution (DE), Intersect Mutation Differential Evolution (IMDE) algorithm, self-adaptive DE (JDE), improved Particle Swarm Optimization (PSO) algorithms, Artificial Bee Colony (ABC) algorithm and Bee Swarm Optimization (BSO) algorithm. Further two real world problems namely Gas Transmission Compressor Design and Optimal Capacity of Gas production facilities are considered and the better performance of DFO is confirmed.