The GPU-based parallel Ant Colony System

Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks ...

GPU parallelization strategies for metaheuristics: a survey

As one of the latest meta-heuristic algorithms, the grasshopper optimization algorithm (GOA) has extensive applications because of its efficiency and simplicity. However, the basic GOA still has enough room for improvement. Therefore, a new variant GOA algorithm which combines two strategies, namely PCA–GOA, is proposed. Firstly, principal component analysis strategy is employed to obtain the grasshoppers with minimally correlated variables, which can improve the exploitation capability of the GOA. Then, a novel inertia weight is proposed to balance exploration and exploitation in an intelligent way, which makes the GOA to have better search capability. Furthermore, the performance of PCA–GOA is evaluated by solving a series of benchmark functions. The experimental results manifest that the PCA–GOA provides better outcomes than the basic GOA and other state-of-the-art algorithms on the majority of functions, which demonstrates the superiority of the PCA–GOA.

Grasshopper optimization algorithm with principal component analysis for global optimization

Ant Colony Optimization (ACO) is a well-established metaheuristic successfully applied to solve hard combinatorial optimization problems, including Travelling Salesman Problem (TSP). However, ACO algorithm as many population-based approaches has some disadvantages, such as lower solution quality and longer computational time. To overcome these issues, parallel Cultural Ant Colony Optimization (pCACO) is introduced in this paper. The proposed approach hybridises Cultural Algorithm with ACO-based \(\mathcal {MAX}\)-\(\mathcal {MIN}\) Ant System. pCACO has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. Through testing nine benchmark asymmetric TSP problems, the experimental results show the new method enhances the solution quality when compared to sequential and parallel ACO, yielding comparable computational time to parallel ACO.

Cultural Ant Colony Optimization on GPUs for Travelling Salesman Problem

Graph Processing Units (GPUs) have recently evolved into a super multi-core and a fully programmable architecture. In the CUDA programming model, the programmers can simply implement parallelism ideas of a task on GPUs. The purpose of this paper is to accelerate Ant Colony Optimization (ACO) for Traveling Salesman Problems (TSP) with GPUs. In this paper, we propose a new parallel method, which is called the Transition Condition Method. Experimental results are extensively compared and evaluated on the performance side and the solution quality side. The TSP problems are used as a standard benchmark for our experiments. In terms of experimental results, our new parallel method achieves the maximal speed-up factor of 4.74 than the previous parallel method. On the other hand, the quality of solutions is similar to the original sequential ACO algorithm. It proves that the quality of solutions does not be sacrificed in the cause of speed-up.

Chien-Ju Wu

Papers

Using CUDA GPU to Accelerate the Ant Colony Optimization Algorithm