Parallel Cooperative Savings Based Ant Colony Optimization — Multiple Search and Decomposition Approaches

Abstract

In this paper we study different parallel implementations of the Savings based Ant System algorithm developed for solving the Vehicle Routing Problem. We analyze the effects of low-level parallelization, multiple search strategies and domain decomposition approaches. For the different strategies speedup and efficiency as well as solution quality are reported. Different information exchanges are analyzed within the multiple search strategies.

Ant Colony Optimization: Overview and Recent Advances

“…They show that the cooperation of multiple homogenous colonies becomes less effective for increasing search effort and stronger local search algorithms. Doerner et al [50] study different frequencies of information exchange using a fixed-frequency policy. Chen and Zhang [33] propose the use of variable frequency schedules for the migration of ants.…”

Ant Colony Optimization: Overview and Recent Advances

“…As shown by several researches [27,31,50,103,115,143], coarse grained parallelization schemes are much more promising for ACO. When applied to ACO, coarse grained schemes run p subcolonies in parallel, where p is the number of available processors.…”

Parallel Ant Colony Optimization: Algorithmic Models and Hardware Implementations

“…The works of Talbi et al [11], Randall and Lewis [12], Islam et al [13], Craus and Rudeanu [14], Stützle [3] and Doerner et al [15] are based on a similar parallelization approach and a distributed memory architecture. Delisle et al [5,6] implemented this scheme on shared-memory architectures like SMP computers and multi-core processors.…”