assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. in this study, we solve quadratic assignment problem (qap), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. it should be noted that any facility must be assign to only one location. in this paper, first o...

in this paper,  a heuristic algorithm is proposed in order to solve a nonlinear lexicography goal programming (nlgp) by using an efficient initial point. some numerical experiments showed that the search quality by the proposed heuristic in a multiple objectives problem depends on the initial point features, so in the proposed approach the initial point is retrieved by data envelopment analysis...

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing strategies and genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to t...

in some industries as foundries, it is not technically feasible to interrupt a processor between jobs. this restriction gives rise to a scheduling problem called no-idle scheduling. this paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. the problem is first mathematically formulated by three different mixed integer linear programming...

Particle swarm optimization (PSO) algorithm is simple stochastic global optimization technique, but it exists unbalanced global and local search ability, slow convergence speed and solving accuracy. An improved simulated annealing (ISAM) algorithm is introduced into the PSO algorithm with crossover and Gauss mutation to propose an improved PSO (ISAMPSO) algorithm based on the mutation operator ...

In this paper, an improved genetic algorithm for multi-object optimization is proposed. Simulated annealing is used to local search in genetic algorithms. Furthermore, fuzzy reasoning is adopted to modify crossover probability and mutation probability according to characteristics of population in genetic algorithms instead of fixed parameters. And so, it can be convergence to global optimum qui...

Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov c...

A Monte Carlo simulated annealing algorithm based on the generalized entropy of Tsallis is presented. The algorithm obeys detailed balance and reduces to a steepest descent algorithm at low temperatures. Application to the conformational optimization of a tetrapeptide demonstrates that the algorithm is more effective in locating low energy minima than standard simulated annealing based on molec...

Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Given a finite number of sensors, optimizing the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. In many working environments, for achieving good coverage, we m...