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Mixed DE – ABC Algorithm for Global Optimization Problems  [cached]
Tarun Kumar Sharma,Millie Pant
International Journal of Artificial Intelligence & Knowledge Discovery , 2011,
Abstract: This paper presents a simple, hybrid two phase optimization algorithm called DE – ABC and Self – Adaptive DE - ABC for solving global optimization problems. DE – ABC consists of alternating phases of Differential Evolution (DE) and Artificial Bee Colony (ABC). The algorithm is designed so as to preserve the strengths of both the algorithms. In this paper we compared the performance of the Self-Adaptive DE – ABC algorithm with that of DE, ABC, and DE – ABC on a set of multidimensional numerical optimization problems. Empirical results show that the proposed Self – Adaptive DEABC is quite competent for solving the problems
An Efficient Algorithm for the Global Optimization Using Order Transformation
Djalil Boudjehem,Nora Mansouri
Asian Journal of Information Technology , 2012,
Abstract: In this study present an efficient algorithm of global search to optimizing in n dimension space. This method is suitable for functions that have many extremes. Present algorithm can determine a narrow space around the global optimum in very restricted time based on a stochastic tests and an adaptive partition of the search space. The present algorithm reduces the order of the optimization problem in hand to order two before it tries to solve it, then the problem will be transformed again into its original order. Thus, the new algorithm has the ability to avoid the local optima and reduces the number of evaluations and improves the speed of the algorithm converge. The proposed algorithm tested to locate the global optimum of different test problems and finally used to identify a physical stress/ strain problem. It was found that the algorithm was effective to locate the global optimum even though the objective function has a large number of optima
Flower Pollination Algorithm for Global Optimization  [PDF]
Xin-She Yang
Physics , 2013, DOI: 10.1007/978-3-642-32894-7_27
Abstract: Flower pollination is an intriguing process in the natural world. Its evolutionary characteristics can be used to design new optimization algorithms. In this paper, we propose a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers. We first use ten test functions to validate the new algorithm, and compare its performance with genetic algorithms and particle swarm optimization. Our simulation results show the flower algorithm is more efficient than both GA and PSO. We also use the flower algorithm to solve a nonlinear design benchmark, which shows the convergence rate is almost exponential.
An ellipsoidal branch and bound algorithm for global optimization  [PDF]
William Hager,Dzung Phan
Mathematics , 2009,
Abstract: A branch and bound algorithm is developed for global optimization. Branching in the algorithm is accomplished by subdividing the feasible set using ellipses. Lower bounds are obtained by replacing the concave part of the objective function by an affine underestimate. A ball approximation algorithm, obtained by generalizing of a scheme of Lin and Han, is used to solve the convex relaxation of the original problem. The ball approximation algorithm is compared to SEDUMI as well as to gradient projection algorithms using randomly generated test problems with a quadratic objective and ellipsoidal constraints.
An Algorithm for Global Optimization Inspired by Collective Animal Behavior
Erik Cuevas,Mauricio González,Daniel Zaldivar,Marco Pérez-Cisneros,Guillermo García
Discrete Dynamics in Nature and Society , 2012, DOI: 10.1155/2012/638275
Abstract: A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
A Social Spider Algorithm for Global Optimization  [PDF]
James J. Q. Yu,Victor O. K. Li
Computer Science , 2015,
Abstract: The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The Social Spider Algorithm is evaluated by a series of widely-used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.
Global Convergence of an Extended Descent Algorithm without Line Search for Unconstrained Optimization  [PDF]
Cuiling Chen, Liling Luo, Caihong Han, Yu Chen
Journal of Applied Mathematics and Physics (JAMP) , 2018, DOI: 10.4236/jamp.2018.61013
Abstract: In this paper, we extend a descent algorithm without line search for solving unconstrained optimization problems. Under mild conditions, its global convergence is established. Further, we generalize the search direction to more general form, and also obtain the global convergence of corresponding algorithm. The numerical results illustrate that the new algorithm is effective.
A new hybrid artificial bee colony algorithm for global optimization  [PDF]
Xiangyu Kong,Sanyang Liu,Zhen Wang
International Journal of Computer Science Issues , 2013,
Abstract: To further improve the performance of artificial bee colony algorithm (ABC), a new hybrid ABC (HABC) for global optimization is proposed via exploring six initialization methods. Furthermore, to balance the exploration and exploitation abilities, a new search mechanism is also developed. The algorithms are applied to 27 benchmark functions with various dimensions to verify its performance. Numerical results demonstrate that the proposed algorithms outperforms the ABC in global optimization problems, especially the HABC algorithm with random initialization and HABCO algorithm with orthogonal initialization.
A Simple But Effective Canonical Dual Theory Unified Algorithm for Global Optimization  [PDF]
Jiapu Zhang
Physics , 2011,
Abstract: Numerical global optimization methods are often very time consuming and could not be applied for high-dimensional nonconvex/nonsmooth optimization problems. Due to the nonconvexity/nonsmoothness, directly solving the primal problems sometimes is very difficult. This paper presents a very simple but very effective canonical duality theory (CDT) unified global optimization algorithm. This algorithm has convergence is proved in this paper. More important, for this CDT-unified algorithm, numerous numerical computational results show that it is very powerful not only for solving low-dimensional but also for solving high-dimensional nonconvex/nonsmooth optimization problems, and the global optimal solutions can be easily and elegantly got with zero dual gap.
Firefly Algorithm, Levy Flights and Global Optimization  [PDF]
Xin-She Yang
Mathematics , 2010,
Abstract: Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
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