H-PSO Routing Optimization Model for Zoomlion Ghana Limited

Abstract

This research combines Particle Swarm Optimization (PSO) with Crossover and Mutation Operators of Genetic Algorithm (GA) to produce a hybrid optimization algorithm to solve a routing problem identified at Zoomlion Ghana Limited, Sekondi Takoradi branch. PSO is known to converge prematurely and can be trapped into a local minimum especially with complex problems. On the other hand, GA is a robust and works well with discrete and continuous problems. The Crossover and Mutation operations of GA makes the iterations converges faster and are reliable. The hybrid algorithm therefore merges these operators into PSO to produce a more reliable optimal solution. The hybrid algorithm was then used to solve the routing problem identified at Zoomlion Ghana Limited, Sekondi Takoradi branch. A total of 160 public waste bin centers scattered in the metropolis and the distance between them were considered. The main aim was to determine the best combination of the set of routes connecting all the bin centers in the municipality that will produce the shortest optimal route for the study. MATLAB simulation was run of the list of distances to determine the optimal route. After 10,000 iterations, PSO produced an optimal result of 81.6 km, GA produced an optimal result of 88.9 km and the proposed hybrid model produced an optimal result of 79.9 km