Search

The optimization of train formation plan is a large-scale combinatorial optimization problem, which is difficult to solve. This paper mainly studies the optimization algorithm of the single-block train formation plan. The corresponding mathematical model is established by consulting relevant literature, and a positive feedback search algorithm based on absolute conditions is proposed. First of all, the wagon flow that meets the absolute conditions directly runs through train flow without making other choices. For the wagon flow that does not meet the absolute conditions, select the target station to reach directly according to the probability. The probability of wagon flow selecting a station is calculated according to the pheromone of the wagon flow at the station. At the same time, a pheromone update strategy with positive feedback mechanism is proposed to make the search process converge. Finally, the feasibility of the algorithm and the necessity of introducing absolute conditions into the algorithm are verified by taking eight technical stations in the linear direction of the road network as examples. [ABSTRACT FROM AUTHOR]

Clustering is inherently a highly challenging research problem. The elastic net algorithm is designed to solve the traveling salesman problem initially, now is verified to be an efficient tool for data clustering in n-dimensional space. In this paper, by introducing a nearest neighbor learning method and a local search preferred strategy, we proposed a new Self-Organizing NN approach, called the Adaptive Clustering Elastic Net (ACEN) to solve the cluster analysis problems. ACEN consists of the adaptive clustering elastic net phase and a local search preferred phase. The first phase is used to find a cyclic permutation of the points as to minimize the total distances of the adjacent points, and adopts the Euclidean distance as the criteria to assign each point. The local search preferred phase aims to minimize the total dissimilarity within each clusters. Simulations were made on a large number of homogeneous and nonhomogeneous artificial clusters in n dimensions and a set of publicly standard problems available from UCI. Simulation results show that compared with classical partitional clustering methods, ACEN can provide better clustering solutions and do more efficiently. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel framework for the parallel solution of combinatorial problems based on MODS theory (PARMODS) This framework makes use of metaheuristics based on the Deterministic Swapping (MODS) theory. These approaches represents the feasible solution space of any combinatorial problem through a Deterministic Finite Automata. Some of those methods are the Metaheuristic Of Deterministic Swapping (MODS), the Simulated Annealing Deterministic Swapping (SAMODS), the Simulated Annealing Genetic Swapping (SAGAMODS) and the Evolutionary Deterministic Swapping (EMODS) Those approaches have been utilized in different contexts such as data base optimization, operational research [1-3,8] and multi-objective optimization. The main idea of this framework is to exploit parallel computation in order to obtain a general view of the feasible solution space of any combinatorial optimization problem. This is, all the MODS methods are used in a unique general optimization process. In parallel, each instance of MODS explores a different region of the solution space. This allows us to explore distant regions of the feasible solution which could not be explored making use of classical (sequential) MODS implementations. Some experiments are performed making use of well-known TSP instances. Partial results shows that PARMODS provides better solutions than sequential MODS based implementations. [ABSTRACT FROM AUTHOR]

In this paper, an improved genetic algorithm (GA) for solving the multi-level uncapacitated facility location problem (MLUFLP) is presented. First improvement is achieved by better implementation of dynamic programming, which speeds up the running time of the overall GA implementation. Second improvement is hybridization of the genetic algorithm with the fast local search procedure designed specially for MLUFLP. The experiments were carried out on instances proposed in the literature which are modified standard single level facility location problem instances. Improved genetic algorithm reaches all known optimal and the best solutions from literature, but in much shorter time. Hybridization with local search improves several best-known solutions for large-scale MLUFLP instances, in cases when the optimal is not known. Overall running time of both proposed GA methods is significantly shorter compared to previous GA approach. [ABSTRACT FROM AUTHOR]

This paper states a novel, Evolutionary Metaheuristic Based on the Automata Theory (EMODS) for the multiobjective optimization of combinatorial problems. The proposed algorithm uses the natural selection theory in order to explore the feasible solutions space of a combinatorial problem. Due to this, local optimums are often avoided. Also, EMODS exploits the optimization process from the Metaheuristic of Deterministic Swapping to avoid finding unfeasible solutions. The proposed algorithm was tested using well known multi-objective TSP instances from the TSPLIB. Its results were compared against others Automata Theory inspired Algorithms using metrics from the specialized literature. In every case, the EMODS results on the metrics were always better and in some of those cases, the distance from the true solutions was 0.89%. [ABSTRACT FROM AUTHOR]

In this paper an electromagnetism-like approach (EM) for solving the low autocorrelation binary sequence problem (LABSP) is applied. This problem is a notoriously difficult computational problem and represents a major challenge to all search algorithms. Although EM has been applied to the topic of optimization in continuous space and a small number of studies on discrete problems, it has potential for solving this type of problems, since movement based on the attraction-repulsion mechanisms combined with the proposed scaling technique directs EM to promising search regions. Fast implementation of the local search procedure additionally improves the efficiency of the overall EM system. [ABSTRACT FROM AUTHOR]

The number of efficient points in criteria space of multiple objective combinatorial optimization problems is considered in this paper. It is concluded that under certain assumptions, that number grows polynomially although the number of Pareto optimal solutions grows exponentially with the problem size. In order to perform experiments, an original algorithm for obtaining all efficient points was formulated and implemented for three classical multiobjective combinatorial optimization problems. Experimental results with the shortest path problem, the Steiner tree problem on graphs and the traveling salesman problem show that the number of efficient points is much lower than a polynomial upper bound. [ABSTRACT FROM AUTHOR]

In this paper we describe the first Ant algorithm designated for fuzzy TSP (FTSP). Our work consists of two parts. The former part is dedicated to FTSP specification. The latter transforms the Ant System (chronologically the first Ant algorithm) in order to tackle the FTSP, and implement the new algorithm on a FTSP instance. [ABSTRACT FROM AUTHOR]