Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel Simulated Annealing (SA) algorithms and compare them on an extensive test bed used previously for the asse...

In this paper a parallel algorithm for simulated annealing (S.A.) in the continuous case, the Multiple Trials and Adaptive Supplementary Search, MTASS algorithm, is presented. It is based on a combination of multiple trials, local improved searchs and an adaptive cooling schedule. The results in optimizing some standard test problems are compared with a sequential S.A. algorithms and another pa...

A delivery problem which reduces to an NP-complete set-partitioning problem is considered. Two algorithms of parallel simulated annealing, i.e. the simultaneous independent searches and the simultaneous periodically interacting searches are investigated. The objective is to improve the accuracy of solutions to the problem by applying parallelism. The accuracy of a solution is meant as its proxi...

This paper analyses alternatives for the parallelization of the Simulated Annealing algorithm when applied to the placement of modules in a VLSI circuit considering the use of PVM on an Ethernet cluster of workstations. It is shown that different parallelization approaches have to be used for high and low temperature values of the annealing process. The algorithm used for low temperatures is an...

In this paper we propose a new parallelization scheme for Simulated Annealing — Hierarchical Parallel SA (HPSA). This new scheme features coarse-granularity in parallelization, directed at message-passing systems such as clusters. It combines heuristics such as adaptive clustering with SA to achieve more efficiency in local search. Through experiments with various optimization problems and comp...

In short-term production planning, jobs are assigned to machines and scheduled, taking into consideration that operations must be performed in pre-defined sequences. Since the problem is NP-hard, heuristics have to be used. Simulated annealing, neural networks and genetic algorithms are some of the recent approaches. We have tried to improve those methods by taking a hybrid of simulated anneali...

We present a simulation/optimization model combining optimization with BIOPLUME II simulation for optimizing in-situ bioremediation system design. In-situ bioremediation of contaminated groundwater has become widely accepted because of its cost-effective ability to achieve satisfactory cleanup. We use parallel recombinative simulated annealing to search for an optimal design and apply the BIOPL...

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...

Solving a discrete optimization problem consists in finding a solution which maximizes (or minimizes) an objective function. The function is often called the fitness and the corresponding landscape the fitness landscape. We are concerned with statistical measures of a fitness landscape in the context of the vehicle routing problem with time windows (VRPTW). The measures are determined by using ...

A new mapping heuristic is developed based on the recently proposed Mean Field Annealing MFA algorithm An e cient implementation scheme which decreases the complexity of the proposed algorithm by asymptotical factors is also given Per formance of the proposed MFA algorithm is evaluated in comparison with two well known heuristics Simulated Annealing and Kernighan Lin Results of the exper iments...