earth dam is a structure as homogeneous or non-homogeneous forms for raising water level or water supply. earth dam consist of different parts that one of the main parts is clay core. choosing an optimal non permeable core which causes reduction of seepage through dam body and also being stable is necessary. the objective of this research is to optimize the geometry of earth dam clay core such ...

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...

This paper proposes a new algorithm of a simulated annealing (SA): Parallel Simulated Annealing using Genetic Crossover (PSA/GAc). The proposed algorithm consists of several processes, and in each process SA is operated. The genetic crossover is used to exchange information between solutions at fixed intervals. While SA requires high computational costs, particularly in continuous problems, thi...

this research presents a new application of the cloud theory-based simulated annealing algorithm to solve mixed model assembly line sequencing problems where line stoppage cost is expected to be optimized. this objective is highly significant in mixed model assembly line sequencing problems based on just-in-time production system. moreover, this type of problem is np-hard and solving this probl...

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...

1. Complexities of Problems and Algorithms 2. Introduction to Global Search Methods 3. Contribution of Statistical Physics and Thermodynamics 4. The Simulated Annealing Algorithm 4.1.The Simulated Annealing Algorithm 4.2.Model Calibration and Algorithm Convergence 5. Examples of Problems Solved Thanks to Simulated Annealing 5.1.The Quadratic Assignment Problem 5.2.The Travelling Salesman Proble...

− In this paper, we propose a Simulated Annealing PolyClonal Selection Algorithm (SAPCSA) for Traveling Salesman Problems (TSP). By introducing a simulated annealing (SA) strategy to the PolyClonal Selection Algorithm (PCSA), the SAPCSA integrate the characteristics of both SA and PCSA. Numerous instances have been simulated to verify the proposed algorithm.

This research presents a new application of the cloud theory-based simulated annealing algorithm to solve mixed model assembly line sequencing problems where line stoppage cost is expected to be optimized. This objective is highly significant in mixed model assembly line sequencing problems based on just-in-time production system. Moreover, this type of problem is NP-hard and solving this probl...

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments show the proposed QA algorithm finds better clustering assignments than SA. Furthermore, QA is as easy as SA to implement.

We introduce four new general optimization algorithms based on the ‘demon’ algorithm from statistical physics and the simulated annealing (SA) optimization method. These algorithms reduce the computation time per trial without significant effect on the quality of solutions found. Any SA annealing schedule or move generation function can be used. The algorithms are tested on traveling salesman p...