New Approach to Optimization by simulating insect in wheat farms
Fariborz Ahmadi, Hamid Salehi · 2014
The aim of optimization is to determine the best-suited solution to a problem under a given set of constraints. Several researchers over the decades have come up with different solutions to linear and non-linear optimization problems. Mathematically an optimization problem involves a fitness function describing the problem, under a set of constraints representing the solution space for the problem. Unfortunately, most of the traditional optimization techniques are centered around evaluating the first derivatives to locate the optima on a given constrained surface. Because of the difficulties in evaluating the first derivatives, to locate the optima for many rough and discontinuous optimization surfaces, in recent times, several derivative free optimization algorithms have emerged. The optimization problem, now-adays, is represented as an intelligent search problem, where one or more agents are employed to determine the optima on a search landscape, representing the constrained surface for the optimization problem. Almost all of the approach to solve NP-hard and NP-complete problem simulate artificial life. In this research, the behavior of eurygaster life is studied, so according to their life the new algorithm is introduced. In spite of PSO algorithm, that is used to solve continuous nonlinear functions, researchers’ algorithm is so suitable to solve both continuous and discrete functions. Eurygasters attack to grain farms and distributed over them. It is worth to mention that these insects attack to farms in groups and each group colonize in one farm. It is observed that after periods of time all of the farms in a region are occupied by these groups of eurygasters. When each group of these insects are going to seek a farm to feed on it, they consider nearly all the farms and settles on a farm which have a lowest distance with them and doesn’t have any group of eurygasters. It is clear that by distributing several groups of eurygasters, depending on the problem size, on search space of problem, the solution of the problem can be extracted. In this research, using the behavior of eurygasters, a new algorithm has been invented and has been tested on graph partitioning. The evaluation results show the advantage of researcher algorithm over ancient ones like genetic and PSO.