optimization methods: Biogeography Based Optimization (BBO), Genetic. Algorithm ..  Ackley, D.H., An empirical study of bit vector function optimization. References [Ackley, ] [Androulakis, ] [Aström, ] [Bäck, ] b] Ackley, D.H., An empirical study of bit vector function optimization. useless and global optimization algorithms are required to obtain a satisfac- [ 19] D. H. Ackley, “An Empirical Study of Bit Vector Function Optimization,”.
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Extensive and intensive studies in this aspect fruit in numerous optimization techniques, particularly, the bioinspired metaheuristic methods which draw inspiration from the means on how humans and living creatures struggle to survive in a challenging environment, for instance, genetic algorithm GA [ 1 ], particle swarm optimization PSO [ 2 ], differential evolution DE [ 3 ], ant colony optimization [ 4 ], artificial bee colony algorithm [ 5 ], and firefly algorithm [ 6 ], form the hot topics in this area.
Adaptation in Natural and Artificial Systems: Cuckoo Search Algorithm The CSA, which draws inspiration from cuckoo’s adaption to breeding and reproduction, is idealized with the assumptions as follows: The multimodal Rastrigin’s function is defined as [ 29 ].
The Ackley’s function is a multimodal function which is described as [ 26 ]. Finally, some conclusions are drawn in Section 5. The improvement over the CSA is tested and validated through the optimization of several benchmarks.
Adaptive Cuckoo Search Algorithm for Unconstrained Optimization
This is probably due to its capability to concurrently refine the local search phase around the current good solutions while exploring more aggressively for the optimal solutions, attributed to the adopted adaptive step size. In the CSA, only the probability of the abandoned nests p a is tuned. Introduction The solutions to multitudinous domains-whether in engineering design, operational research, industrial process, or economics inevitably have optimization at heart.
Kennedy J, Eberhart R. Open in a separate window. For both the CSA and ACSA, the Euclidean distance from the funcgion global minimum to the location of the best host nest with the lowest fitness value is evaluated in each iteration. It is worth mentioning that through empirical simulations, increasing the maximum step size value will encourage a more thorough global exploration and eventually lead to faster convergence; however, the generated new solutions might fall outside the design domain for some cases.
Nevertheless, in real world situations, obtaining the exact global optimum optumization impracticable, as the optimizatoin problems are always subjected to various uncertainties and constraints. Modification of the intensification and diversification approaches in the blt developed cuckoo search algorithm CSA is performed. Undoubtedly, its popularity increases unceasingly in the not-to-distant future. Goyal S, Patterh MS. International Journal of Mobile Communications.
“Removing Genetics from the Standard Genetic Algorithm – Related Papers”
In this case, instead of finding the actual optimum, the core consideration in selecting an appropriate optimization technique is how much improvement is big for a given application at a plausible computational complexity, with an acceptable error.
The modified CSA, specifically, the adaptive cuckoo search algorithm ASCAis proposed in Section 3and the comparative results in evaluating the benchmark optimization functions are presented in Section 4.
Considering Table 1 which summarizes the average cycles needed for both the algorithms to meet the stopping criterion, it can be clearly seen that the CSA takes more iteration to converge; however, the proposed ACSA reaches the global optima about one time faster on average. Yang X-S, Deb S.
The simulations of all algorithms are performed for 30 independent runs with the number of fitness function evaluations is set to Transactions of the Japanese Society for Artificial Intelligence. The CSA, which draws inspiration from cuckoo’s adaption to breeding and reproduction, is idealized with the assumptions as follows:. As the simplest unimodal test function, the de Jong’s function is given by [ 27 ].
Verified email at cs. Abstract Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm CSA is performed. The best fitness value in each iteration is evaluated, and its average at each iteration from all the 30 trials is the measured. This is the region where the global minimum is resided. Artificial bee colony ABC algorithm is one of the popular swarm intelligence algorithms.
In this paper, by scrutinizing the advantages and the limitations of the standard CSA, a new modified CSA, specifically the ACSA, which adopts an adjustable step size in its computation, has been proposed.
This is presumably due to the CSA is getting trapped in local solution, as there are many local minima present in this test function. On the contrary, the ACSA with adaptive step size control strategy performs more rigorous search through the solution space.
As shown in Table 1the ACSA reaches the known global optimum in a mean of cycles, while the CSA requires a longer processing time in order to converge. The ACSA is able to find the global solution, approximately after fitness function evaluations, as opposed to CSA which reaches the global solution after objective function evaluations.
To evaluate the feasibility of the proposed ACSA, the algorithm is applied to optimize the five benchmark functions with known global optima, where two of which are unimodal and three of which are multimodal. However, the search process may be time consuming, due to the associated random walk behavior [ 24 ].
Genetic Algorithms and Simulated Annealing. The obtained average best fitness values at fixed iteration number of 1,and in optimizing the benchmark functions are summarized in Table 2.
In fact, as shown in Table 2the DE, EP, and PSO algorithms usually converge faster initially, but they often get stuck in local optima functtion, which is particularly obvious in the pptimization of Ackley, de Jong, and Griewank’s functions.
The author declares that there is no conflict of interests regarding the publication of this paper. First, we propose a generation alternation model called JGG just generation gap suited for multi-parental crossovers. The JGG replaces parents with children completely every generation. Start with a population of possible solutions, a new and potentially better hh cuckoo egg is generated.
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Get my own profile Cited by View all All Since Citations h-index 22 13 iindex 32 The following articles are fnction in Scholar. Such idealized assumptions in the CSA, similarly to what was previously proposed in other metaheuristic optimization approaches, make use of the ideas of elitism, intensification, and diversification. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces.