Domain based multiple ant colony Algorithm for discrete structural Optimization a metaheuristic approach

Abstract

newlineMany research works have been successfully carried out on newlineoptimum structural designs by means of several soft computing tools such as newlineGenetic Algorithm GA Simulated Annealing SA and so on Most of these newlineoptimization techniques encounter some common limitations such as newlineenormous computational time premature convergence and algorithm newlinetermination at sub optimal points Therefore the chance of generating a newlineglobal optimum or a near optimal solution in a reasonable amount of newlinecomputational time becomes less newlineMore recently the Ant Colony Optimization ACO algorithm newlineinspired from the foraging strategy of social ants is gaining much popularity newlineover other heuristic approaches due to its peculiar characteristics ACO is a newlinemetaheuristic decentralized and combinatorial optimization algorithm by newlinewhich a wide spectrum of problems has been successfully solved in the field newlineof networking and only limited research has been carried out in the field of newlinestructural optimization Hence, in the present work an attempt has been made newlineto develop an enhanced version of ACO namely Domain Based Multiple newlineAnt Colony Optimization DB MACO algorithm for structural newlineOptimization DB MACO algorithm has been developed by blending multiple ant newlinecolonies with two powerful local search procedures such as Tabu Search TS newlineand Cost Minimization algorithm CM algorithm newline newline