Metaheuristics are general problem solving algorithms which abstract from the actual problem description. Therefore they can be easily applied to many optimization problems. Simulated annealing is a simple and fast metaheuristic with an analogy to metal processing. As metal particles generate a solid and regular structure when cooling slowing simulated annealing seeks a low-energy solution avoi...

This paper presents a simulated annealing algorithm that based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm also can solve those problems that can be solved by local search algorithm. Various experimental results show that the new algorithm can actually give more satisfactory solutions than general simulated annealing algorithm can do.

Simulated annealing is a well-studied local search metaheuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a mechanism to escape local optima by allowing hill-climbing moves (i.e., moves which worsen the objective function value) in hopes of finding a global optimum. A brief history of simulated...

We analyze simulated annealing applied to multiple-valued programmable logic array (MVL PLA) design. Of spec@c interest is the use of parallel processors. We consider the use of loosely-coupled, coarsegrainedparallel systems, and study the relationship between the quality of the solution and computation time, on the one hand, and simulated annealing parameters, start temperature, cooling rate, ...

This research presents a Simulated Annealing based technique to address the assembly line balancing problem for multiple objective problems when paralleling of workstations is permitted. The Simulated Annealing methodology is used for 23 line balancing strategies across seven problems. The resulting performance of each solution was studied through a simulation experiment. Many of the problems c...

In today’s competitive transportation systems, passengers search to find traveling agencies that are able to serve them efficiently considering both traveling time and transportation costs. In this paper, we present a new model for the traveling salesman problem with multiple transporters (TSPMT). In the proposed model, which is more applicable than the traditional versions, each city has diffe...

Two well known stochastic optimization algorithms, simulated annealing and genetic algorithm are compared when using a sample to minimize an objective function which is the expectation of a random variable. Since they lead to minimum depending on the sample, a weighted version of simulated annealing is proposed in order to reduce this kind of overt bias. The algorithms are implemented on an opt...

Proteins or genes that have similar sequences are likely to perform the same function. One of the most widely used techniques for sequence comparison is sequence alignment. Sequence alignment allows mismatches and insertion/deletion, which represents biological mutations. Sequence alignment is usually performed only on two sequences. Multiple sequence alignment, is a natural extension of two-se...