This paper presents a genetic algorithm (GA) search method in order to obtain better circuit implementation of the mixed polarity Reed-Muller functions. By combining global searching ability of genetic algorithm and local searching ability of simulated annealing, the proposed GA method could achieve fast convergence. It differs to traditional genetic algorithm, in which the proposed GA forms an...

this paper deals with the problem of user-server assignment in online social network systems. online social network applications such as facebook, twitter, or instagram are built on an infrastructure of servers that enables them to communicate with each other. a key factor that determines the facility of communication between the users and the servers is the expected transmission time (ett). a ...

Population annealing is a Monte Carlo algorithm that marries features from simulated-annealing and parallel-tempering Monte Carlo. As such, it is ideal to overcome large energy barriers in the free-energy landscape while minimizing a Hamiltonian. Thus, population-annealing Monte Carlo can be used as a heuristic to solve combinatorial optimization problems. We illustrate the capabilities of popu...

We propose a new graph based strategy for the detection of spatial clusters of arbitrary geometric form in a map of geo-referenced populations and cases. Our test statistic is based on the likelihood ratio test previously formulated by Kulldorff and Nagarwalla for circular clusters. A new technique of adaptive simulated annealing is developed, focused on the problem of finding the local maxima ...

This paper reconsiders the travelling tournament problem, a complex sport-scheduling application which has attracted significant interest recently. It proposes a population-based simulated annealing algorithm with both intensification and diversification. The algorithm is organized as a series of simulated annealing waves, each wave being followed by a macro-intensification. The diversification...

In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions.A simulated annealing algorithm for the clustering problem. The solution of the clustering problem usually solved by the K-means algorithm.In this paper, we explore the applicability of simulated annealing. Clustering problem is investigat...

In this paper, we propose sequential Monte Carlo simulated annealing (SMC-SA), a populationbased simulated annealing algorithm, for continuous global optimization. SMC-SA incorporates the sequential Monte Carlo method to track the converging sequence of Boltzmann distributions in simulated annealing, such that the empirical distribution will converge weakly to the uniform distribution on the se...

In this paper, we propose a population-based optimization algorithm, Sequential Monte Carlo Simulated Annealing (SMC-SA), for continuous global optimization. SMC-SA incorporates the sequential Monte Carlo method to track the converging sequence of Boltzmann distributions in simulated annealing. We prove an upper bound on the difference between the empirical distribution yielded by SMC-SA and th...

This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into subpopulations, and in each subpopulation the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighbo...

This paper addresses the Vehicle Routing Problem with Delivery Time Cost. This problem aims to find a set of routes of minimal total costs including the travelling cost and delivery time cost, starting and ending at the depot, in such a way that each customer is visited by one vehicle given the capacity of the vehicle to satisfy a specific demand. In this research, a hybrid metaheuristic approa...