A multilevel evolutionary algorithm for optimizing numerical functions

This is a study on the effects of multilevel selection (MLS) theory in optimizing numerical functions. Based on this theory, a Multilevel Evolutionary Optimization algorithm (MLEO) is presented. In MLEO, a species is subdivided in cooperative populations and then each population is subdivided in groups, and evolution occurs at two levels so called individual and group levels. A fast population dynamics occurs at individual level. At this level, selection occurs among individuals of the same group. The popular genetic operators such as mutation and crossover are applied within groups. A slow population dynamics occurs at group level. At this level, selection happens among groups of a population. The group level operators such as regrouping, migration, and extinction-colonization are applied among groups. In regrouping process, all the groups are mixed together and then new groups are formed. The migration process encourages an individual to leave its own group and move to one of its neighbour groups. In extinction-colonization process, a group is selected as extinct, and replaced by offspring of a colonist group. In order to evaluate MLEO, the proposed algorithms were used for optimizing a set of well known numerical functions. The preliminary results indicate that the MLEO theory has positive effect on the evolutionary process and provide an efficient way for numerical optimization

Affectation des locomotives aux trains

Spécifications physiques et données du réseau -- Objectifs et restrictions du problème -- Module stratégique -- Modèle général -- Adaptation au secteur ferroviaire -- Approche de résolution -- Stratégie de branchement -- Expériences numériques -- Obtention de solutions réalisables en permettant des retards de train -- Solution réalisable heuristique -- Qualité de la solution -- Expériences numériques -- Amélioration du modèle en introduisant des coupes -- Premier consist réalisable -- Facettes des consists réalisables -- Branchement -- Résultats numériques -- Module tactique -- Les objectifs et les contraintes du module tactique -- Modélisation -- Méthode de résolution -- Expériences numériques

A rank based particle swarm optimization algorithm with dynamic adaptation

AbstractThe particle swarm optimization (PSO) technique is a powerful stochastic evolutionary algorithm that can be used to find the global optimum solution in a complex search space. This paper presents a variation on the standard PSO algorithm called the rank based particle swarm optimizer, or PSOrank, employing cooperative behavior of the particles to significantly improve the performance of the original algorithm. In this method, in order to efficiently control the local search and convergence to global optimum solution, the γ best particles are taken to contribute to the updating of the position of a candidate particle. The contribution of each particle is proportional to its strength. The strength is a function of three parameters: strivness, immediacy and number of contributed particles. All particles are sorted according to their fitness values, and only the γ best particles will be selected. The value of γ decreases linearly as the iteration increases. A time-varying inertia weight decreasing non-linearly is introduced to improve the performance. PSOrank is tested on a commonly used set of optimization problems and is compared to other variants of the PSO algorithm presented in the literature. As a real application, PSOrank is used for neural network training. The PSOrank strategy outperformed all the methods considered in this investigation for most of the functions. Experimental results show the suitability of the proposed algorithm in terms of effectiveness and robustness

A Novel Technique for Compressing Pattern Databases in the Pancake Sorting Problems

In this paper we present a lossless technique to compress pattern databases (PDBs) in the Pancake Sorting problems. This compression technique together with the choice of zero-cost operators in the construction of additive PDBs reduces the memory requirement for PDBs in these problems to a great extent, thus making otherwise intractable problems able to be efficiently handled. Also, using this method, we can construct some problem-size independent PDBs. This precludes the necessity of constructing new PDBs for new problems with different numbers of pancakes. In addition to our compression technique, by maximizing over the heuristic value of additive PDBs and the modified version of the gap heuristic, we have obtained powerful heuristics for the burnt pancake problem

Artificial Bee colony for resource constrained project scheduling problem

Solving resource constrained project scheduling problem (RCPSP) has important role in the context of project scheduling. Considering a single objective RCPSP, the goal is to find a schedule that minimizes the makespan. This is NP-hard problem (Blazewicz et al., 1983) and one may use meta-heuristics to obtain a global optimum solution or at least a near-optimal one. Recently, various meta-heuristics such as ACO, PSO, GA, SA etc have been applied on RCPSP. Bee algorithms are among most recently introduced meta-heuristics. This study aims at adapting artificial bee colony as an alternative and efficient optimization strategy for solving RCPSP and investigating its performance on the RCPSP. To evaluate the artificial bee colony, its performance is investigated against other meta-heuristics for solving case studies in the PSPLIB library. Simulation results show that the artificial bee colony presents an efficient way for solving resource constrained project scheduling problem

A Branch-First, Cut-Second Approach for Locomotive Assignment

The problem of assigning locomotives to trains consists of selecting the types and number of engines that minimize the fixed and operational locomotive costs resulting from providing sufficient power to pull trains on fixed schedules. The force required to pull a train is often expressed in terms of horsepower and tonnage requirements rather than in terms of number of engines. This complicates the solution process of the integer programming formulation and usually creates a large integrality gap. Furthermore, the solution of the linearly relaxed problem is strongly fractional. To obtain integer solutions, we propose a novel branch-and-cut approach. The core of the method consists of branching decisions that define on one branch the projection of the problem on a low-dimensional subspace. There, the facets of the polyhedron describing a restricted constraint set can be easily derived. We call this approach branch-first, cut-second. We first derive facets when at most two types of engines are used. We then extend the branching rule to cases involving additional locomotive types. We have conducted computational experiments using actual data from the Canadian National railway company. Simulated test-problems involving two or more locomotive types were solved over 1-, 2-, and 3-day rolling horizons. The cuts were successful in reducing the average integrality gap by 52% for the two-type case and by 34% when more than 25 types were used. Furthermore, the branch-first, cut-second approach was instrumental in improving the best known solution for an almost 2,000-leg weekly problem involving 26 locomotive types. It reduced the number of locomotives by 11, or 1.1%, at an equivalent savings of $3,000,000 per unit. Additional tests on different weekly data produced almost identical results.integer linear programming, branch and cut, decomposition, scheduling, railway