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...

In this paper, two heuristic optimization techniques are tested and compared in the application of motion planning for autonomous agricultural vehicles: Simulated Annealing and Genetic Algorithms. Several preliminary experimentations are performed for both algorithms, so that the best neighborhood definitions and algorithm parameters are found. Then, the two tuned algorithms are run extensively...

In this paper, a model is presented to locate ambulances, considering backup facility (to increase reliability) and the restriction of ambulance capacity. This model is designed for emergencies. In this model the covered demand for each demand point depends on the number of coverage times and the amount of demand. The demand amount and ambulance coverage radius are considered...

Using a distributed algorithm rather than a centralized one can be extremely bene cial in large search problems. In addition, the incorporation of machine learning techniques like Reinforcement Learning (RL) into search algorithms has often been found to improve their performance. In this article we investigate a search algorithm that combines these properties by employing RL in a distributed m...

A vehicle routing problem is a significant problem that has attracted great attention from researchers in recent years. The main objectives of the vehicle routing problem are to minimize the traveled distance, total traveling time, number of vehicles and cost function of transportation. Reducing these variables leads to decreasing the total cost and increasing the driver's satisfaction level. O...

Stochastic optimization methods such as evolutionary algorithms and Markov Chain Monte Carlo methods usually involve a Markov search of the optimization domain. Evolutionary annealing is an evolutionary algorithm that leverages all the information gathered by previous queries to the cost function. Evolutionary annealing can be viewed either as simulated annealing with improved sampling or as a ...

This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup tim...

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...

This paper proposes Parallel Simulated Annealing using Genetic Crossover (PSA/GAc). In this algorithm, there are several processes of Simulated Annealing (SA) working parallel. To exchange information between the solutions, the operation of genetic crossover is performed. Through the continuous test problems, it is found that PSA/GAc can search the solution effectively. The proposed algorithm i...

Since the logistics is an industry with relatively higher energy consumption, the building industry lowcarbon logistics ought to attract wide attentions from the building enterprises. Simulated annealing is an effective intelligent algorithm for optimizing the building materials logistics route. This paper analyzes the similarities between the process of the building materials logistics route o...