Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there...
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ISBN:
(纸本)9781479949557
Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC algorithm, which is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. For these insufficiencies, we propose a novel artificial bee colony algorithm (NABC) for numericaloptimization problems in this paper to improve the exploitation capability by incorporating the current best solution into the search procedure. Experiments are conducted on a set of unimodal/multimodal benchmark functions. The experiments results of NABC have been compared with Gbest-guided artificial bee colony algorithm (G-ABC), improved artificial bee colony algorithm (I-ABC), Elitist artificial bee colony algorithm (E-ABC). The results show that NABC is superior to those algorithms in most of the tested functions.
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, tw...
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Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are modified and new configurations are used. The modified versions are tested on some well-known benchmark functions. Results show that the new changes have positive effects on the performance of ABC algorithm. (C) 2013 Published by Elsevier Inc.
Differential evolution (DE) is one of the most powerful stochastic search methods which was introduced originally for continuous optimization. In this sense, it is of low efficiency in dealing with discrete problems. ...
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Differential evolution (DE) is one of the most powerful stochastic search methods which was introduced originally for continuous optimization. In this sense, it is of low efficiency in dealing with discrete problems. In this paper we try to cover this deficiency through introducing a new version of DE algorithm, particularly designed for binary optimization. It is well-known that in its original form, DE maintains a differential mutation, a crossover and a selection operator for optimizing non-linear continuous functions. Therefore, developing the new binary version of DE algorithm, calls for introducing operators having the major characteristics of the original ones and being respondent to the structure of binary optimization problems. Using a measure of dissimilarity between binary vectors, we propose a differential mutation operator that works in continuous space while its consequence is used in the construction of the complete solution in binary space. This approach essentially enables us to utilize the structural knowledge of the problem through heuristic procedures, during the construction of the new solution. To verify effectiveness of our approach, we choose the uncapacitated facility location problem (UFLP)-one of the most frequently encountered binary optimization problems-and solve benchmark suites collected from OR-Library. Extensive computational experiments are carried out to find out the behavior of our algorithm under various setting of the control parameters and also to measure how well it competes with other state of the art binary optimization algorithms. Beside UFLP, we also investigate the suitably of our approach for optimizing numericalfunctions. We select a number of well-known functions on which we compare the performance of our approach with different binary optimization algorithms. Results testify that our approach is very efficient and can be regarded as a promising method for solving wide class of binary optimization problems.
optimization plays a critical role in human modern life. Nowadays, optimization is used in many aspects of human modern life including engineering, medicine, agriculture and economy. Due to the growing number of optim...
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optimization plays a critical role in human modern life. Nowadays, optimization is used in many aspects of human modern life including engineering, medicine, agriculture and economy. Due to the growing number of optimization problems and their growing complexity, we need to improve and develop theoretical and practical optimization methods. Stochastic population based optimization algorithms like genetic algorithms and particle swarm optimization are good candidates for solving complex problems efficiently. Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by the social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multi-modal functions. In this paper, a new method for improving PSO has been introduced. The Proposed method which has been named Light Adaptive Particle Swarm optimization is a novel method that uses a fuzzy control system to conduct the standard algorithm. The suggested method uses two adjunct operators along with the fuzzy system in order to improve the base algorithm on global optimization problems. Our approach is validated using a number of common complex uni-modal/multi-modal benchmark functions and results have been compared with the results of Standard PSO (SPSO2011) and some other methods. The simulation results demonstrate that results of the proposed approach is promising for improving the standard PSO algorithm on global optimization problems and also improving performance of the algorithm.
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 sy...
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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.
the paper proposes a stochastic differential search operator (SDSO) which can traverse the numerical object space (eg. R ''), and a Multi-Role particle swarm which can be differentiated into the different role...
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ISBN:
(纸本)9783037853849
the paper proposes a stochastic differential search operator (SDSO) which can traverse the numerical object space (eg. R ''), and a Multi-Role particle swarm which can be differentiated into the different roles to search the objective space using the different strategies. Based on the SDSO and the Multi-Role particle swarm, a Multi-Role particle swarm algorithm (MRPSA) for numericaloptimization is proposed. In the test experiment, 6 unconstrained benchmark functions are used to demonstrate the performance of MRPSA. The results show that MRPSA can find the optimal or close-to-optimal solutions of those benchmark functions efficiently.
Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good at ...
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ISBN:
(纸本)9780769548111
Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good at exploration but poor at exploitation. An improved ABC algorithm called Gbest-guided ABC (GABC) was introduced by researchers to improve the exploitation of ABC algorithm. In order to improve the GABC algorithm further, we propose an improved GABC algorithm with a linear weight called WGABC, and introduce a novel solution search equation used at scout bee stage of WGABC algorithm. Experimental results tested on a set of numerical benchmark functions show that WGABC can outperform ABC and GABC algorithms in most of the experiments.
Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good...
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Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good at exploration but poor at exploitation. An improved ABC algorithm called Gbestguided ABC (GABC) was introduced by researchers to improve the exploitation of ABC algorithm. In order to improve the GABC algorithm further, we propose an improved GABC algorithm with a linear weight called WGABC, and introduce a novel solution search equation used at scout bee stage of WGABC algorithm. Experimental results tested on a set of numerical benchmark functions show that WGABC can outperform ABC and GABC algorithms in most of the experiments.
To improve the exploration and exploitation abilities of the standard Gravitational Search Algorithm (GSA), a novel operator called "Disruption", originating from astrophysics, is proposed. The disruption op...
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To improve the exploration and exploitation abilities of the standard Gravitational Search Algorithm (GSA), a novel operator called "Disruption", originating from astrophysics, is proposed. The disruption operator is inspired by nature and, with the least computation, has improved the ability of GSA to further explore and exploit the search space. The proposed improved GSA has been evaluated on 23 nonlinear benchmark functions and compared with standard GSA, the genetic algorithm and particle swarm optimization. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. (C) 2011 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by social behavior of bird flocks and...
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Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multimodal functions. To improve PSO performance on global optimization problems, this paper proposes a novel approach, called Plowing PSO algorithm, through introducing a new operator to PSO. The proposed approach combines the exploration ability of random search with the features of PSO. Our approach is validated using ten common complex unimodal/multimodal benchmark functions. The simulation results demonstrate that the proposed approach is superior in avoiding premature convergence to standard PSO, and five variation of it. Therefore, the Plowing PSO algorithm is successful in improving standard PSO to solve complex numerical function optimization problems.
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