• A NOVEL MULTI-ANT COLONY OPTIMIZATION FOR MULTI-OBJECTIVE RESOURCE ALLOCATION PROBLEMS

Abstract

This paper presents a novel multi-ant colony optimization (NM-ACO) for solving multi-objective resource allocation Problems (MORAPs). The proposed algorithm differs from the traditional ones in its design the vector of colonies associated with the vector of objective functions as well as the MORAP is formulating as a connected graph ,where the ant construct the solution by assigning the amount of resource to the  stage by roaming on connected graph . On the other hand, the local search scheme which makes the ants moves to new rich regions. Moreover, the proposed algorithm introduced an extended memory to store global Pareto solutions to reduce computational time. Effectiveness and efficiency of proposed algorithm was validated by comparing the result of NM-ACO with multi-objective hybrid genetic algorithm (mohGA) which was applied to MORAP later. Also the comparative study demonstrated the superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems.