There are three types of simulated annealing: i) classical simulated annealing ii) fast simulated annealing and iii) generalized simulated annealing. Simulated annealing is a meta-heuristic method that solves global optimization problems. Many problems in biology, physics, mathematics, and engineering, demand the determination of the global optimum of multidimensional functions. Zhu Y, Zhong Y (2009) An improved simulated annealing algorithm. Zhang C, Li Q, Wang W et al (2017) Immune particle swarm optimization algorithm based on adaptive search. An improved self-adaptive PSO algorithm with detection function for multimodal function optimization problems. Improved strategy of adaptive particle swarm optimization based on simulated annealing. Yang H, Yang Y, Yang Z et al (2014) A particle swarm optimization algorithm based on simulated annealing. Wang H, Cao C (2005) Parallel particle swarm optimization based on simulated annealing. Sadati N, Amraee T, Ranjbar AM (2009) A global particle swarm-based-simulated annealing optimization technique for under-voltage load shedding problem. Pan Q, Wang W, Zhu J (2006) Hybrid scheduling algorithm based on particle swarm optimization and simulated annealing. Evol Comput 2009 CEC '09 IEEE Cong IEEE : 381–388 Li C, Yang S (2009) An adaptive learning particle swarm optimizer for function optimization//. Lei X, Shi Z (2008) Application and parameter analysis of particle swarm optimization algorithm in function optimization. Acta Applicandae Mathematica 12(1):108–111 Laarhoven PJM, Aarts EHL (1988) Simulated annealing: theory and applications. Proc 1995 IEEE Int Conf Neural Netw (Perth, Australia), Nov. Jamili A, Shafia MA, Tavakkoli-Moghaddam R (2011) A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem. Huang S (2009) A review of particle swarm optimization algorithm. Higashi N, Iba H (2013) Particle swarm optimization with Gaussian mutation. Han XL (2008) Particle swarm-simulated annealing fusion algorithm and its application in function optimization. Gao S, Yang J, Wu X et al (2005) Particle swarm optimization based on simulated annealing algorithm. Gao Y, XIE S (2004) Particle swarm optimization algorithm based on simulated annealing. Paper presented at the Evol Comput, 2001. Comput Sci 45(2)Įberhart SY (2002) Particle swarm optimization: developments, applications and resources. Int J Swarm Intell Res 1(4):46–61ĭong H, Li D, Zhang X (2018) Particle swarm optimization algorithm for dynamically adjusting inertia weight. IEEE PressĬlerc M, Shi Y (2012) Beyond standard particle swarm optimisation. Expert Syst Appl 38(12):14439–14450Ĭlerc M, Kennedy J (2002) The particle swarm - explosion, stability, and convergence in a multidimensional complex space. Genet Evol Comput ConfĬhen SM, Chien CY (2011) Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. Comput Optim Appl 29(3):369–385īergh F, Engelbrecht AP (2001) Effects of swarm size on cooperative particle swarm Optimisers. Adv Eng Softw 47(1):1–6īen-Ameur W (2004) Computing the initial temperature of simulated annealing. (1991) Optimization by simulated annealing: an experimental evaluation// At&t Bell Labs :215–226īank M, Fatemi Ghomi SMT, Jolai F et al (2012) Application of particle swarm optimization and simulated annealing algorithms in flow shop scheduling problem under linear deterioration. And it has competitive potential for solving other complicated optimization problems.Īarts E, Korst J (1990) Simulated annealing and Boltzman machine Īragon CR, Johnson DS, Mcgeoch LA et al. Compared with traditional PSO, the SA-PSO has improvement over effectiveness and accuracy to some extent. The results illustrated that SA-PSO had a stronger ability to avoid prematurity and get rid of local optimum. In this paper, several classic unimodal/multimodal functions were used to simulate the SA-PSO algorithm. The given algorithm owned the abilities of both increasing the diversity of particle swarm and jumping out of the local optimum. As the result of that, the improved algorithm, combined SA with PSO, would be given in this paper. It allows the SA algorithm to escape from local minimum. Classically, the probability to accept bad solutions is high at the beginning. So a SA-PSO algorithm would be proposed in this paper. Besides that, it has slow convergence speed and poor accuracy during the late evolutionary period. While solving the optimization problems of complex functions, particle swarm optimization (PSO) would be easy to fall into trap in the local optimum.
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