An integral approach for Geno-Simulated Annealing

Abstract

Global optimization is the problem of finding the global optimum of any given function in a certain search space. Simulated Annealing (SA) and Genetic Algorithms (GA) are among the well-known techniques used for global optimization. Adjusting the parameters of SA such as the temperature schedule and the neighborhood range plays an important role in the performance of the algorithm. Furthermore, many studies in literature showed that the best values for SA parameters depend on the optimization problem. We introduce a novel hybrid approach that uses SA to solve an optimization problem and uses GA simultaneously to adapt the parameters of SA. This new approach is referred to as Geno-Simulated Annealing (GSA). It does not require any predefined values for the parameters of SA. To evaluate the performance of the proposed approach, we used seven well-known benchmark optimization functions. The obtained results indicate the superiority of the proposed approach as compared to a similar approach and to conventional SA.

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

“…Digalakis and Margaritis developed two algorithms titled as the generational replacement model (GRM) and the steady state replacement model by making modifications on the genetic algorithm and monitored their performances on unconstrained test functions [12]. By combining the GA and SA algorithms, Hassan et al proposed the geno-simulated annealing (GSA) algorithm and implemented it on the most commonly used unconstrained test functions [13]. In order to obtain a better performance in the multidimensional search space, Chatterjee et al suggested the nonlinear variation of the known PSO, the non-PSO algorithm, and measured its performance on several unconstrained test functions [14].…”