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Author: search.filters.author.Chutchavong, VanvisaSubject: search.filters.subject.Optimization

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    An improved performance simulated annealing based on evolution strategies for single objective optimization problems
    (2020-07-01)
    Chutchavong, Vanvisa
    ;
    Pumee, Thanapoom
    ;
    Thongkrairat, Somsin
    ;
    Anuwongpinit, Thanavit
    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.
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    A time improvement PSO base algorithm using multithread programming
    (2019-12-01)
    Thongkrairat, Somsin
    ;
    Chutchavong, Vanvisa
    This paper proposes a method to improve the processing time of algorithm that bases on Particle Swarm Optimization (PSO) such as Original PSO, AS-PSO (Ant Supervised by Particle Swarm optimization), ACO (ant colony optimization) by using multithread programming technique. Typically, the PSO Base algorithm creates several particles to find the optimized solution. Each particle owns its local process and resource, but original implementation has been processed in a single thread or sequential computation system. That causes the system to take a longer time to compute when increasing the number of particles. It makes the system slow down when implementing a massive amount of particles and ambiguous to estimated efficiency for each algorithm. This method takes advantage of a multithread computing system for utilizing resources on the system to reduce computation time. The proposed method use thread as particle to manage local resource and implement multithreading system and use thread synchronization system to manage global resource and exchange data between particle. To measure the efficiency of this method Traveling Salesman Problem (TSP) used to be a problem to optimize and compare computation time between the original method and the proposed method. The experimental result show correctness and time improvement of the proposed method compared with the original method.