An Improved Performance Simulated Annealing Based On Evolution Strategies for Single Objective Optimization Problems

This paper presents solutions for single objective optimization problems with developed algorithm from simulated annealing based on a simple (μ + λ) -ES, It is divided into two algorithms, separated mutation (SM1) and survival mutation (SM2). After that, compared with randomized local search and simulated annealing. The test function is part of the IEEE WCCI 2020 on the topic of CEC-C2 single objective bound constrained optimization. This research has chosen the basic functions in the test such as Bent cigar function, rastrigin function, high conditioned elliptic function, HGBat function, rosenbrock’s function, griewank’s function, discus function, expanded schaffer’s function, weierstrass function, sphere function, natyas function, lévi function N.13, himmelblau’s function, and three-hump camel function. These functions are attract attention and competition. A results of SM1 and SM2 can solve single objective optimization problems better than RLS and SA. In high conditioned elliptic, The fitness value of RLS is equal to 3.96E-11, The fitness value of SA is equal to 8.12E-10, The fitness value of SM1 is equal to 5.39E-14 and The fitness value of SM2 is equal to 9.70E-15, It let us show the efficiency of SM2 that can get better results than SM1.