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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 Hybrid Tabu Search and Particle warm Optimization Algorithm for Mixed Discrete Optimization Problems
ZHANG Xing-hui,BAI Fu-sheng
Journal of Chongqing Normal University , 2011,
Abstract: Particle swarm optimization (PSO) algorithm is mainly used to find global olutions of continuous variables optimization problems. In this paper, the penalty function approach to handle the discrete variables is employed, in which mixed discrete optimization problem:*,n is handled as continuous one:*,n. Standard PSO algorithm will likely fall into local optimal solution and exist premature convergence. Tabu search(TS) algorithm has good hill-climbing ability and can escape from the local optimal solution and turn to other parts of the solution space. A neighborhood structure is designed and a hybrid tabu search and particle swarm optimization (TS-PO) algorithm is proposed , which has memory ability and efficient hill-climbing capability. Simulation reults on Rosenbrocks function and pressure vessel design show that the disadvantage of getting in the local best point of standard PSO is overcome effectively and the ability of global optimality is toned up.(* Indicates a formula, please see the full text)
Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm  [PDF]
Ting-Cheng Feng, Tzuu-Hseng S. Li
Applied Mathematics (AM) , 2016, DOI: 10.4236/am.2016.75040
Abstract: This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.
A New Method for Global Optimization Problems  [PDF]
WU Zhi-you
Journal of Chongqing Normal University , 2009,
Abstract: Ii is well known that necessarv conditions are the main tools for the development of efficient numerical methods in local optimzation. This paper introduces a new method for global optimization problems: some optimization methods for a kind of {0,1} quadraticprogramming problemS with mixed VariableS are Studied by using the glObal Optimality conditions (neSSary global optimality condition [NC] and sufficient global optimality condition[SC]).Firstly a local optimization method LOMMQP is designed according to its necessary global optimulily conditions. Then a special auxiliarv function F(x)is designed Lo escape the current local minimizes. Finally a global optimization method(COM)with some stopping criterion is presented by using Lhe obtained auxiliary function F(x),the local optimization method LOMand the sufficient global optimality condition[SC].
A Robust Archived Differential Evolution Algorithm for Global Optimization Problems  [cached]
Zhangjun Huang,Cheng-en Wang,Mingxu Ma
Journal of Computers , 2009, DOI: 10.4304/jcp.4.2.160-167
Abstract: A robust archived differential evolution algorithm is put forward by means of embedding a flexibility processing operator and an efficiency processing operator based on original DE and ADE. A special constraint-handling mechanism based on dynamic penalty functions and fitness calculation of individuals is adopted in the proposed method to deal with various constraints effectively, which is further extended by means of a flexibility processing operator so as to make it suitable for different type problems, including those with or without constraint(s) and those with continuous, discrete or mixed discrete-continuous variables. Furthermore, an archive of solutions is maintained during the evolutionary process so as to keep the useful information of previous solutions and local optima for the estimation of new solutions. Based on the archive of solutions, an iterative control operator and an efficiency processing operator are designed in the algorithm. The former guides the evolutionary process towards a promising search space, avoiding unnecessary and worthless search. The latter improves the local searching efficiency and the final searching quality. Experimental results based on a suite of six well-known optimization problems reveal that the proposed algorithm is robust, effective, efficient and suitable for different type global optimization problems.
Particle swarm optimization based on artificial bee colony for solving engineering constrained optimization problems

WANG Ke-ke,LV Qiang,ZHAO Han-qing,BAI Fan,

计算机应用研究 , 2012,
Abstract: In order to solve engineering constrained optimization problems,this paper proposed a hybrid method combining particle swarm optimization(PSO) and artificial bee colony(ABC).The method selected the better particles in PSO as food sources for ABC algorithm,and used the tabu table to save the local optimization results in order to avoid PSO trapping into local optimum.And it used a feasibility-based rule to solve constrained problems,and divided the particle swarm into feasible subpopulation and infeasible subpopulation.So it produced the new food sources containing the information of good feasible and infeasible solution in the process of ABC,which could make up for the feasibility-based rule being invalid when the optimum was close to the boundary of constraint conditions.The algorithm was validated using four standard engineering design problems.The results indicate that PSO-ABC algorithm can find out better optimum and has stronger solidity.
Particle-swarm optimization algorithm with mixed search

LIAN Zhi-gang,JIAO Bin,

控制理论与应用 , 2010,
Abstract: A mixed search particle-swarm optimization algorithm(MSPSO) is proposed by combining the algorithms for individual optimization, global optimization and generation-population optimization. In the simulations by using benchmark non-linear test functions, experiment data and convergence curves show that this new algorithm is effective, rapidly convergent in optima search.
An Integrated GA-ABC Optimization Technique to Solve Unit Commitment and Economic Dispatch Problems  [PDF]
P. Surekha,N. Archana,S. Sumathi
Asian Journal of Scientific Research , 2012,
Abstract: Unit Commitment (UC) and Economic Load Dispatch (ELD) problems are significant research areas to determine the economical generation schedule with all generating unit constraints, such as unit ramp rates, unit minimum and maximum generation capabilities and minimum up-time and down-time. This study proposed a technique for solving the UC and ELD problems using bio-inspired techniques like Genetic Algorithm (GA) and Artificial Bee Colony (ABC) Optimization. The experiments are performed in two phases: UC phase and ELD phase. In the UC phase, a turn-on and turn-off schedule for a given combination of generating units is performed using GA, thus satisfying a set of dynamic operational constraints. During the second ELD phase, the pre-committed schedules are optimized and the optimal load is distributed among the scheduled units using ABC algorithm. The effectiveness of the proposed technique is investigated on two test systems namely, IEEE 30 bus system and ten unit system. Experimental results prove that the proposed method is capable of yielding higher quality solution including mathematical simplicity, fast convergence, diversity maintenance, robustness and scalability for the complex UC-ELD problem.
Recent Advances in Global Optimization for Combinatorial Discrete Problems  [PDF]
Adel R. Awad, Samia O. Chiban
Applied Mathematics (AM) , 2015, DOI: 10.4236/am.2015.611162
Abstract: The optimization of discrete problems is largely encountered in engineering and information domains. Solving these problems with continuous-variables approach then convert the continuous variables to discrete ones does not guarantee the optimal global solution. Evolutionary Algorithms (EAs) have been applied successfully in combinatorial discrete optimization. Here, the mathematical basics of real-coding Genetic Algorithm are presented in addition to three other Evolutionary Algorithms: Particle Swarm Optimization (PSO), Ant Colony Algorithms (ACOA) and Harmony Search (HS). The EAs are presented in as unifying notations as possible in order to facilitate understanding and comparison. Our combinatorial discrete problem example is the famous benchmark case of New-York Water Supply System WSS network. The mathematical construction in addition to the obtained results of Real-coding GA applied to this case study (authors), are compared with those of the three other algorithms available in literature. The real representation of GA, with its two operators: mutation and crossover, functions significantly faster than binary and other coding and illustrates its potential as a substitute to the traditional optimization methods for water systems design and planning. The real (actual) representation is very effective and provides two near-optimal feasible solutions to the New York tunnels problem. We found that the four EAs are capable to afford hydraulically-feasible solutions with reasonable cost but our real-coding GA takes more evaluations to reach the optimal or near-optimal solutions compared to other EAs namely the HS. HS approach discovers efficiently the research space because of the random generation of solutions in every iteration, and the ability of choosing neighbor values of solution elements “changing the diameter of the pipe to the next greater or smaller commercial diameter” beside keeping good current solutions. Our proposed promising point to improve the performance of GA is by introducing completely new individuals in every generation in GA using a new “immigration” operator beside “mutation” and “crossover”.
Multi Circle Detection on Images Using Artificial Bee Colony (ABC) Optimization  [PDF]
Erik Cuevas,Felipe Sencion-Echauri,Daniel Zaldivar,Marco Perez Cisneros
Computer Science , 2014,
Abstract: Hough transform (HT) has been the most common method for circle detection, exhibiting robustness, but adversely demanding considerable computational effort and large memory requirements. Alternative approaches include heuristic methods that employ iterative optimization procedures for detecting multiple circles. Since only one circle can be marked at each optimization cycle, multiple executions must be enforced in order to achieve multi detection. This paper presents an algorithm for automatic detection of multiple circular shapes that considers the overall process as a multi-modal optimization problem. The approach is based on the artificial bee colony (ABC) algorithm, a swarm optimization algorithm inspired by the intelligent foraging behavior of honey bees. Unlike the original ABC algorithm, the proposed approach presents the addition of a memory for discarded solutions. Such memory allows holding important information regarding other local optima which might have emerged during the optimization process. The detector uses a combination of three non-collinear edge points as parameters to determine circle candidates. A matching function (nectar- amount) determines if such circle candidates (bee-food-sources) are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the ABC algorithm so that the best candidate (global optimum) can be fitted into an actual circle within the edge only image. Then, an analysis of the incorporated memory is executed in order to identify potential local optima, i.e., other circles.
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