An intelligent information sharing strategy within a swarm for unconstrained and constrained optimization problems

Abstract

 In this paper we present a new multilevel information sharing strategy within a swarm to handle single objective, constrained and unconstrained optimization problems. A swarm is a collection of individuals having a common goal to reach the best value (minimum or maximum) of a function. Among the individuals in a swarm, there are some better performers (leaders) those that set the direction of search for the rest of the individuals. An individual that is not in the better performer list (BPL) improves its performance by deriving information from its closest neighbor in BPL. In an unconstrained problem, the objective values are the performance measures used to generate the BPL while a multilevel Pareto ranking scheme is implemented to generate the BPL for constrained problems. The information sharing strategy also ensures that all the individuals in the swarm are unique as in a real swarm, where at a given time instant two individuals cannot share the same location. The uniqueness among the individuals result in a set of near optimal individuals at the final stage that is useful for sensitivity analysis. The benefits of the information sharing strategy within a swarm are illustrated by solving two unconstrained problems with multiple equal and unequal optimum, a constrained optimization problem dealing with a test function and a well studied welded beam design problem.