共查询到20条相似文献,搜索用时 15 毫秒
1.
S. Talatahari B. Farahmand AzarR. Sheikholeslami A.H. Gandomi 《Communications in Nonlinear Science & Numerical Simulation》2012,17(3):1312-1319
A novel chaotic improved imperialist competitive algorithm (CICA) is presented for global optimization. The ICA is a new meta-heuristic optimization developed based on a socio-politically motivated strategy and contains two main steps: the movement of the colonies and the imperialistic competition. Here different chaotic maps are utilized to improve the movement step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the other ICA-based methods demonstrates the superiority of the CICA for the benchmark functions. 相似文献
2.
针对目前混沌优化算法在选取局部搜索空间时的盲目性,提出一种具有自适应调节局部搜索空间能力的多点收缩混沌优化方法.该方法在当前搜索空间搜索时保留多个较好搜索点,之后利用这些点来确定之后的局部搜索空间,以达到对不同的函数和当前搜索空间内已进行搜索次数的自适应效果.给出了该算法以概率1收敛的证明.仿真结果表明该算法有效的提高了混沌优化算法的性能,改善了混沌算法的实用性. 相似文献
3.
Guopu Zhu 《Applied mathematics and computation》2010,217(7):3166-3173
Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments. 相似文献
4.
An algorithm called DE-PSO is proposed which incorporates concepts from DE and PSO, updating particles not only by DE operators but also by mechanisms of PSO. The proposed algorithm is tested on several benchmark functions. Numerical comparisons with different hybrid meta-heuristics demonstrate its effectiveness and efficiency. 相似文献
5.
Qingyang Xu Song Wang Li Zhang Ying Liang 《Communications in Nonlinear Science & Numerical Simulation》2013,18(11):3046-3060
Making use of ergodicity and randomness of chaos, a novel chaos danger model immune algorithm (CDMIA) is presented by combining the benefits of chaos and danger model immune algorithm (DMIA). To maintain the diversity of antibodies and ensure the performances of the algorithm, two chaotic operators are proposed. Chaotic disturbance is used for updating the danger antibody to exploit local solution space, and the chaotic regeneration is referred to the safe antibody for exploring the entire solution space. In addition, the performances of the algorithm are examined based upon several benchmark problems. The experimental results indicate that the diversity of the population is improved noticeably, and the CDMIA exhibits a higher efficiency than the danger model immune algorithm and other optimization algorithms. 相似文献
6.
The filled function method is considered as an efficient approach to solve the global optimization problems. In this paper, a new filled function method is proposed. Its main idea is as follows: a new continuously differentiable filled function with only one parameter is constructed for unconstrained global optimization when a minimizer of the objective function is found, then a minimizer of the filled function will be found in a lower basin of the objective function, thereafter, a better minimizer of the objective function will be found. The above process is repeated until the global optimal solution is found. The numerical experiments show the efficiency of the proposed filled function method. 相似文献
7.
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm 总被引:36,自引:0,他引:36
Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.
An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive
is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the
intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the
results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm
(PS-EA) have been compared. The results showed that ABC outperforms the other algorithms. 相似文献
8.
Efficient line search algorithm for unconstrained optimization 总被引:6,自引:0,他引:6
A new line search algorithm for smooth unconstrained optimization is presented that requires only one gradient evaluation with an inaccurate line search and at most two gradient evaluations with an accurate line search. It terminates in finitely many operations and shares the same theoretical properties as the standard line search rules like the Armijo-Goldstein-Wolfe-Powell rules. This algorithm is especially appropriate for the situation when gradient evaluations are very expensive relative to function evaluations.The authors would like to thank Margaret Wright and Jorge Moré for valuable comments on earlier versions of this paper. 相似文献
9.
The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in the ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose a modified ABC algorithm (denoted as ABC/best), which is based on that each bee searches only around the best solution of the previous iteration in order to improve the exploitation. In addition, to enhance the global convergence, when producing the initial population and scout bees, both chaotic systems and opposition-based learning method are employed. Experiments are conducted on a set of 26 benchmark functions. The results demonstrate good performance of ABC/best in solving complex numerical optimization problems when compared with two ABC based algorithms. 相似文献
10.
This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. 相似文献
11.
In this article, a new metaheuristic optimization algorithm is introduced. This algorithm is based on the ability of shark, as a superior hunter in the nature, for finding prey, which is taken from the smell sense of shark and its movement to the odor source. Various behaviors of shark within the search environment, that is, sea water, are mathematically modeled within the proposed optimization approach. The effectiveness of the suggested approach is compared with many other heuristic optimization methods based on standard benchmark functions. Also, to illustrate the efficiency of the proposed optimization method for solving real‐world engineering problems, it is applied for the solution of load frequency control problem in electrical power systems. The obtained results confirm the validity of the proposed metaheuristic optimization algorithm. © 2014 Wiley Periodicals, Inc. Complexity 21: 97–116, 2016 相似文献
12.
A new approach is proposed for global optimization problems with fuzzy cost functions and fuzzy box and equality constraints. It allows one to avoid complex operations with fuzzy sets and the use of various subjective indices of choice. To resolve the contradiction between economically better solutions with low possibility of realization and a little poorer solution with higher possibility of realization, the synthetic realization is defined as certain fixed -level cut for all membership functions. Consideration of such realizations guarantees a level of credibility not less than given (0, 1] for all globally optimal solutions. Then, so defined -cuts are rectified to cut off realizations with possibility less than and to retain higher possibility realizations which are assigned credibility μ = 1 for the whole interval of possible realizations. This construction results in a set-valued band of credibility not less than for a given fuzzy cost function f̃(x) which band has crisp Lipschitz continuous lower- and upper-value functions f*(x), f*(x) such that f*(x) ≤ f̃(x) ≤ f*(x) for all x X̃ Rn. Then, the gamma algorithm is applied to obtain the interval global optimal solution f̄0(x) = [f0*(x), f*0(x)]. To further simplify the computations, the fuzziness in the feasible set X̃ is transferred to the function value space transforming X̃ into the crisp unit cube in Rn+ common for all fuzzy optimization problems in Rn with box and equality constraints. 相似文献
13.
This paper proposes an efficient implementation of a trust-region-like algorithm. The trust region is restricted to an appropriately chosen two-dimensional subspace. Convergence properties are discussed and numerical results are reported.The numerical experiments were performed on the Data General MV-8000 computer at the Center for Operations Research and Econometrics, Université Catholique de Louvain, and financed by Services de la Programmation de la Politique Scientifique under Contract No. 80-85/12. The authors are grateful for the support. 相似文献
14.
In this paper, a new gradient-related algorithm for solving large-scale unconstrained optimization problems is proposed. The new algorithm is a kind of line search method. The basic idea is to choose a combination of the current gradient and some previous search directions as a new search direction and to find a step-size by using various inexact line searches. Using more information at the current iterative step may improve the performance of the algorithm. This motivates us to find some new gradient algorithms which may be more effective than standard conjugate gradient methods. Uniformly gradient-related conception is useful and it can be used to analyze global convergence of the new algorithm. The global convergence and linear convergence rate of the new algorithm are investigated under diverse weak conditions. Numerical experiments show that the new algorithm seems to converge more stably and is superior to other similar methods in many situations. 相似文献
15.
We present an algorithm for finding the global maximum of a multimodal, multivariate function for which derivatives are available. The algorithm assumes a bound on the second derivatives of the function and uses this to construct an upper envelope. Successive function evaluations lower this envelope until the value of the global maximum is known to the required degree of accuracy. The algorithm has been implemented in RATFOR and execution times for standard test functions are presented at the end of the paper.Partially supported by NSF DMS-8718362. 相似文献
16.
The aim of this paper is to incorporate the preconditioned gradient path in a nonmonotone stabilization algorithm for unconstrained optimization. The global convergence and locally superlinear convergence are established for this class of algorithms. Finally, we report in details the numerical results which show the effectiveness of the proposed algorithm. 相似文献
17.
A DERIVATIVE-FREE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION 总被引:1,自引:0,他引:1
Peng Yehui Liu Zhenhai 《高校应用数学学报(英文版)》2005,20(4):491-498
In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don't bear. 相似文献
18.
Neculai Andrei 《Numerical Algorithms》2008,47(2):143-156
Another hybrid conjugate gradient algorithm is subject to analysis. The parameter β
k
is computed as a convex combination of (Hestenes-Stiefel) and (Dai-Yuan) algorithms, i.e. . The parameter θ
k
in the convex combination is computed in such a way so that the direction corresponding to the conjugate gradient algorithm
to be the Newton direction and the pair (s
k
, y
k
) to satisfy the quasi-Newton equation , where and . The algorithm uses the standard Wolfe line search conditions. Numerical comparisons with conjugate gradient algorithms show
that this hybrid computational scheme outperforms the Hestenes-Stiefel and the Dai-Yuan conjugate gradient algorithms as well
as the hybrid conjugate gradient algorithms of Dai and Yuan. A set of 750 unconstrained optimization problems are used, some
of them from the CUTE library.
相似文献
19.
This paper presents extended artificial physics optimization (EAPO), a population-based, stochastic, evolutionary algorithm (EA) for multidimensional search and optimization. EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm by including each individual’s best fitness history. Including the history improves EAPO’s search capability compared to APO. EAPO and APO invoke a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which EAPO is guaranteed to converge. Discrete-time linear system theory is used to develop a second-order difference equation for an individual’s stochastic position vector as a function of time step. Stable solutions require eigenvalues inside the unit circle, leading to explicit convergence criteria relating the run parameters {mi, w, G}. EAPO is tested against several benchmark functions with excellent results. The algorithm converges more quickly than APO and with better diversity. 相似文献
20.
A comparative study of Artificial Bee Colony algorithm 总被引:27,自引:0,他引:27
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. 相似文献