基于改进花朵授粉算法的智能系统
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摘要
智能系统试图模拟人类专家来解决复杂的现实问题。问题的领域从工程、工业到医学、教育都各不相同。在大多数情况下,系统需要根据多个输入进行决策;但是搜索空间通常很大,因此很难使用传统的算法进行决策。元启发式算法可以用作寻找最优解的一种工具。因此,改进元启发式技术和现有算法是必要的。介绍了一种改进的花朵授粉算法(FPA)。将标准的FPA与克隆选择算法(CSA)结合,应用到23个优化基准函数上;并对其进行测试。将改进算法与五种著名的优化算法(模拟退火、遗传算法、花授粉算法、蝙蝠算法和萤火虫算法)进行比较。实验结果表明,相比标准FPA和其他四种方法,改进花朵授粉算法能够找到更精确的解。
Intelligent systems try to solve complex real-world problems like experts. The problems are different from different areas. In most situations,the system is required to take decisions based on multiple inputs,but the search space is usually very huge so that it will be very hard to use the traditional algorithms to take a decision; at this point,the metaheuristic algorithms can be used as an alternative tool to find near-optimal solutions. Thus,inventing new metaheuristic techniques and enhancing the current algorithms is necessary. An enhanced variant of the flower pollination algorithm( FPA) was introduced. The standard FPA with the clonal selection algorithm( CSA)was hybridized and tested the new algorithm by applying it to 23 optimization benchmark problems. The proposed algorithm is compared with five famous optimization algorithms,namely,simulated annealing,genetic algorithm,flower pollination algorithm,bat algorithm,and firefly algorithm. The results show that the proposed algorithm is able to find more accurate solutions than the standard FPA and the other four techniques. The superiority of the proposed algorithm nominates it for being a part of intelligent and expert systems.
Intelligent systems try to solve complex real-world problems like experts. The problems are different from different areas. In most situations,the system is required to take decisions based on multiple inputs,but the search space is usually very huge so that it will be very hard to use the traditional algorithms to take a decision; at this point,the metaheuristic algorithms can be used as an alternative tool to find near-optimal solutions. Thus,inventing new metaheuristic techniques and enhancing the current algorithms is necessary. An enhanced variant of the flower pollination algorithm( FPA) was introduced. The standard FPA with the clonal selection algorithm( CSA)was hybridized and tested the new algorithm by applying it to 23 optimization benchmark problems. The proposed algorithm is compared with five famous optimization algorithms,namely,simulated annealing,genetic algorithm,flower pollination algorithm,bat algorithm,and firefly algorithm. The results show that the proposed algorithm is able to find more accurate solutions than the standard FPA and the other four techniques. The superiority of the proposed algorithm nominates it for being a part of intelligent and expert systems.
引文
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13 Rezaee A.Brainstorm optimisation algorithm(BSOA):An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems.International Journal of Electrical Power&Energy Systems,2015;69:48-57
14 Saremi S,Mirjalili S,Lewis A.Biogeography-based optimisation with chaos.Neural Computing and Applications,2014;25:1077-1097
15 Wang G G,Gandomi A H,Alavi A H.An effective krill herd algorithm with migration operator in biogeography-based optimization.Applied Mathematical Modelling,2014;38:2454-2462
16王志刚.基于粒子群和人工蜂群算法的混合优化算法.科学技术与工程,2012;12(20):4921-4927Wang Zhigang.Hybrid optimization algorithm based on partical swarm optimization and artificial bee colony algotithm.Science Technology and Engineering,2012;12(20):4921-4927
17 Waser N M.Flower constancy:Definition,cause,and measurement.The American Naturalist,1986;127:593-603
18 Yang X S.Firefly algorithm,stochastic test functions and design optimisation.International Journal of Bio-Inspired Computation,2010;(2):78-84
19 Yu H S.An optimization algorithm based on brainstorming process.International Journal of Swarm Intelligence Research,2011;(2):35-62
20 Hosseini H S.Principal components analysis by the galaxy-based search algorithm:A novel metaheuristic for continuous optimisation.International Journal of Computational Science and Engineering,2011;(6):132-140
21 Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:Artificial bee colony(ABC)algorithm.Journal of Global Optimization,2007;39:459-471
22 Kirkpatrick S,Gelatt C D,Vecchi M P.Optimization by simulated annealing.Science,1983;220:671-680
23 Krishnanand K N,Ghose D.Glowworm swarm optimisation:A new method for optimising multi-modal functions.International Journal of Computational Intelligence Studies,2009;(1):93-119
24 Layeb A A.Clonal selection algorithm based tabu search for satisfiability problems.Journal of Advances in Information Technology,2012;(3):138-146
2 Civicioglu P.Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm.Computers&Geosciences,2012;46:229-247
3 Civicioglu P.Artificial cooperative search algorithm for numerical optimization problems.Information Sciences,2013;229:58-76
4 Cuevas E,Oliva D,Zaldivar D,et al.Circle detection using electromagnetism optimization.Information Sciences,2012;182:40-55
5 Derrac J,García S,Molina D,et al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms.Swarm and Evolutionary Computation,2011;(1):3-18
6黄爱辉.决策树C4.5算法的改进及应用.科学技术与工程,2009;9(1):34-38Huang Aihui.C4.5algorithm of decision tree improvement and application.Science Technology and Engineering,2009;9(1):34-38
7 Fekety F R.The clonal selection theory of acquired immunity.Yale Journal of Biology and Medicine,1960;32:480-491
8 Gandomi A H,Alavi A H.Krill herd:A new bio-inspired optimization algorithm.Communications in Nonlinear Science and Numerical Simulation,2012;17:4831-4845
9 Gao S,Wang W,Dai H,et al.Improved clonal selection algorithm combined with ant colony optimization.Transactions on Information and Systems,2008;91:1813-1823
10 García S,Molina D,Lozano M,et al.A study on the use of nonparametric tests for analyzing the evolutionary algorithms’behaviour:A case study on the CEC’2005 special session on real parameter optimization.Journal of Heuristics,2009;15:617-644
11 Ghaemi M,Derakhshi M R.Forest optimization algorithm.Expert Systems with Applications,2014;41:6676-6687
12 Pavlyukevich I.Lévy flights,non-local search and simulated annealing.Journal of Computational Physics,2007;226:1830-1844
13 Rezaee A.Brainstorm optimisation algorithm(BSOA):An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems.International Journal of Electrical Power&Energy Systems,2015;69:48-57
14 Saremi S,Mirjalili S,Lewis A.Biogeography-based optimisation with chaos.Neural Computing and Applications,2014;25:1077-1097
15 Wang G G,Gandomi A H,Alavi A H.An effective krill herd algorithm with migration operator in biogeography-based optimization.Applied Mathematical Modelling,2014;38:2454-2462
16王志刚.基于粒子群和人工蜂群算法的混合优化算法.科学技术与工程,2012;12(20):4921-4927Wang Zhigang.Hybrid optimization algorithm based on partical swarm optimization and artificial bee colony algotithm.Science Technology and Engineering,2012;12(20):4921-4927
17 Waser N M.Flower constancy:Definition,cause,and measurement.The American Naturalist,1986;127:593-603
18 Yang X S.Firefly algorithm,stochastic test functions and design optimisation.International Journal of Bio-Inspired Computation,2010;(2):78-84
19 Yu H S.An optimization algorithm based on brainstorming process.International Journal of Swarm Intelligence Research,2011;(2):35-62
20 Hosseini H S.Principal components analysis by the galaxy-based search algorithm:A novel metaheuristic for continuous optimisation.International Journal of Computational Science and Engineering,2011;(6):132-140
21 Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:Artificial bee colony(ABC)algorithm.Journal of Global Optimization,2007;39:459-471
22 Kirkpatrick S,Gelatt C D,Vecchi M P.Optimization by simulated annealing.Science,1983;220:671-680
23 Krishnanand K N,Ghose D.Glowworm swarm optimisation:A new method for optimising multi-modal functions.International Journal of Computational Intelligence Studies,2009;(1):93-119
24 Layeb A A.Clonal selection algorithm based tabu search for satisfiability problems.Journal of Advances in Information Technology,2012;(3):138-146