Carpooling has gained considerable importance as an effective solution for reducing pollution, mitigation of traffic and congestion on the roads, reduced demand parking facilities, lesser energy fuel consumption most importantly, reduction in carbon emission, thus improving quality life cities. This work presents a hybrid GA-A* algorithm to obtain optimal routes carpooling problem domain multio...

In recent years, cloud computing plays a crucial role in many real applications. Thus, how to solve workflow scheduling problems, i.e., allocating and different resources, under the environment becomes more important. Although some evolutionary algorithms (EAs) can problems with small scale, they show disadvantages on larger scale this paper, multi-objective genetic algorithm (MOGA) is applied ...

A new hybrid multi-objective, multivariable optimizer utilizing Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Differential Evolution (NSDE), and Multi-Objective Particle Swarm (MOPSO) has been created and tested. The optimizer features automatic switching among these algorithms to expedite the convergence of the optimal Pareto front in the objective function(s) space. The...

The agent technology and genetic algorithms are integrated and is applied to solve multi-objective optimization problem. An agent in this algorithm represents a candidate solution to the multi-objective optimization problem. Agent lives in the grid environment and it possesses own local space called the neighborhood. In the neighborhood, an agent can compete and collaborate with other agents to...

Integrated production-distribution planning (PDP) is one of the most important approaches in supply chain networks. We consider a supply chain network (SCN) to consist of multi suppliers, plants, distribution centers (DCs), and retailers. A bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs in ...

The aim of this paper is to clearly demonstrate the importance of finding a good balance between genetic search and local search in the implementation of hybrid evolutionary multi-criterion optimization (EMO) algorithms. We first modify the local search part of an existing multi-objective genetic local search (MOGLS) algorithm. In the modified MOGLS algorithm, the computation time spent by loca...