A Computationally Efficient Vector Optimizer Using Ant Colony Optimizations Algorithm for Multobjective Designs

An efficient vector optimizer is proposed based on the hybridization of an ant colony optimization method and a novel exploiting search mechanism. To inherit the learning and searching power of an ant colony algorithm while excluding the usage of a tedious and awkward pheromone updating scheme, it is proposed that an algorithm that models the foraging strategy of pachycodyla apicalis ants is employed and modified. In order to yield better Pareto solutions, the gradient balance concept is used to design the exploitation search process in which some a priori information about the characteristics of the objective functions is used in the selection of nests for subsequent intensifying searches. Numerical experiments are reported to validate the merits and advantages of the proposed vector optimizer for solving practical engineering design problems.