F.2.7. Optimization
M. YousefiKhoshbakht; N. Mahmoodi Darani
Abstract
Abstract: The Open Vehicle Routing Problem (OVRP) is one of the most important extensions of the vehicle routing problem (VRP) that has many applications in industrial and service. In the VRP, a set of customers with a specified demand of goods are given and a depot where a fleet of identical capacitated ...
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Abstract: The Open Vehicle Routing Problem (OVRP) is one of the most important extensions of the vehicle routing problem (VRP) that has many applications in industrial and service. In the VRP, a set of customers with a specified demand of goods are given and a depot where a fleet of identical capacitated vehicles is located. We are also given the ‘‘traveling costs’’ between the depot and all the customers, and between each pair of customers. In the OVRP against to VRP, vehicles are not required to return to the depot after completing service. Because VRP and OVRP belong to NP-hard Problems, an efficient hybrid elite ant system called EACO is proposed for solving them in the paper. In this algorithm, a modified tabu search (TS), a new state transition rule and a modified pheromone updating rule are used for more improving solutions. These modifications lead that the proposed algorithm does not trapped at the local optimum and discovers different parts of the solution space. Computational results on fourteen standard benchmark instances for VRP and OVRP show that EACO finds the best known solutions for most of the instances and is comparable in terms of solutions quality to the best performing published metaheuristics in the literature.
H.3.15.3. Evolutionary computing and genetic algorithms
Sh. Lotfi; F. Karimi
Abstract
In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving ...
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In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving multi-objective optimization problems (MOPs) with two or three conflicting objectives. This improved hybrid algorithm, namely MOEA/D-TS, uses the parallel computing capacity of MOEA/D along with the neighborhood search authority of TS for discovering Pareto optimal solutions. Our goal is exploiting the advantages of evolutionary algorithms and TS to achieve an integrated method to cover the totality of the Pareto front by uniformly distributed solutions. In order to evaluate the capabilities of the proposed method, its performance, based on the various metrics, is compared with SPEA, COMOEATS and SPEA2TS on well-known Zitzler-Deb-Thiele’s ZDT test suite and DTLZ test functions with separable objective functions. According to the experimental results, the proposed method could significantly outperform previous algorithms and produce fully satisfactory results.
