A Population-Based Simulated Annealing Algorithm for Global Optimization

Simulated annealing (SA) is a solo-search algorithm, trying to simulate the cooling process of molten metals through annealing to find the optimum solution in an optimization problem. SA selects a feasible starting solution, produces a new solution at the vicinity of it, and makes a decision by some rules to move to the new solution or not. However, the results found by SA depend on the selection of the starting point and the decisions SA makes. In this paper, in order to ameliorate the drawbacks of the algorithm, a population-based simulated annealing (PSA) algorithm is proposed. PSA uses the population's ability to seek different parts of the search space, thus hedging against bad luck in the initial solution or the decisions. A set of benchmark functions was used in order to evaluate the performance of PSA algorithm. Simulation results accentuate the superior capability of PSA in comparison with the other optimization algorithms.