Particle swarm optimization (PSO) algorithm is simple stochastic global optimization technique, but it exists unbalanced global and local search ability, slow convergence speed and solving accuracy. An improved simulated annealing (ISAM) algorithm is introduced into the PSO algorithm with crossover and Gauss mutation to propose an improved PSO (ISAMPSO) algorithm based on the mutation operator and simulated annealing in this paper. In the ISAMPSO algorithm, the mutation operator of genetic algorithm is introduced into the SA algorithm as a generation mechanism of new solution in order to propose an improved simulated annealing algorithm with mutation (ISAM). Then the ISAM algorithm is introduced into the PSO algorithm to jump out the local optimum, effectively achieve the global optimum adjust and optimize the population, maintain the diversity of the population, improve the local search ability and convergence speed. Six classical functions are selected to test the performance of the proposed ISAMPSO algorithm. The simulation experiments results show that the proposed ISAMPSO algorithm can effectively overcomes the stagnation phenomenon and enhance the global search ability. The convergence speed and accuracy were better than the PSO algorithm.
보안공학연구지원센터(IJMUE) [Science & Engineering Research Support Center, Republic of Korea(IJMUE)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Multimedia and Ubiquitous Engineering
간기
월간
pISSN
1975-0080
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Multimedia and Ubiquitous Engineering Vol.10 No.10