Particle swarm optimization (PSO) and artificial bee colony (ABC) are new optimization methods that have attracted increasing research interests because of its simplicity and efficiency. However, when being applied to...
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Particle swarm optimization (PSO) and artificial bee colony (ABC) are new optimization methods that have attracted increasing research interests because of its simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optimal because of its low global exploration efficiency;ABC algorithm has slower convergence speed in some cases because of the lack of powerful local exploitation capacity. In this paper, we propose a hybrid algorithm called PS-ABC, which combines the local search phase in PSO with two global search phases in ABC for the global optimum. In the iteration process, the algorithm examines the aging degree of pbest for each individual to decide which type of search phase (PSO phase, onlooker bee phase, and modified scout bee phase) to adopt. The proposed PS-ABC algorithm is validated on 13 high-dimensional benchmark functions from the IEEE-CEC 2014 competition problems, and it is compared with ABC, PSO, HPA, ABC-PS and OXDE algorithms. Results show that the PS-ABC algorithm is an efficient, fast converging and robust optimization method for solving high-dimensional optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.
As the number of dimensions of an optimization problem increases, the process of deriving the solution becomes more complicated and difficult. Metaheuristic algorithms are effective methods for solving many optimizati...
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As the number of dimensions of an optimization problem increases, the process of deriving the solution becomes more complicated and difficult. Metaheuristic algorithms are effective methods for solving many optimizationproblems, whereas most of these algorithms perform poorly on high-dimensionalproblems. In this paper, a novel metaheuristic algorithm called Dynamic Stochastic Search (DSS) is proposed for high-dimensional optimization problems. To effectively execute the exploration (diversification) and exploitation (intensification) processes of DSS, a dynamic stochastic search process is designed by defining a stochastic search control factor, a search process based on the Gaussian distribution, two shrink modes inspired by the Whale optimization Algorithm, and a balance mechanism derived from the Bat Algorithm. The proposed algorithm has the following advantages: no specific control parameters other than the population size and the maximum number of iterations, a simple structure, and less computational effort in the implementation. Analyzing the computational complexity of DSS demonstrates its simplicity. To evaluate the performance of DSS, twenty 300-dimensional and 3000-dimensional classical benchmark functions as well as thirty 100-dimensional CEC2014 benchmark functions are utilized. The statistical results prove the effectiveness and feasibility of DSS for high-dimensional optimization problems because it shows better convergence performance and higher efficiency than various advanced optimization algorithms. Moreover, the superiority of DSS in solving high-dimensional real-world optimizationproblems is validated by applying it to feature selection for ten real-world benchmark classification datasets from the UCI machine learning repository. (c) 2022 Elsevier B.V. All rights reserved.
Similar to other swarm-based algorithms, the recently developed whale optimization algorithm (WOA) has the problems of low accuracy and slow convergence. It is also easy to fall into local optimum. Moreover, WOA and i...
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Similar to other swarm-based algorithms, the recently developed whale optimization algorithm (WOA) has the problems of low accuracy and slow convergence. It is also easy to fall into local optimum. Moreover, WOA and its variants cannot perform well enough in solving high-dimensional optimization problems. This paper puts forward a new improved WOA with joint search mechanisms called JSWOA for solving the above disadvantages. First, the improved algorithm uses tent chaotic map to maintain the diversity of the initial population for global search. Second, a new adaptive inertia weight is given to improve the convergence accuracy and speed, together with jump out from local optimum. Finally, to enhance the quality and diversity of the whale population, as well as increase the probability of obtaining global optimal solution, opposition-based learning mechanism is used to update the individuals of the whale population continuously during each iteration process. The performance of the proposed JSWOA is tested by twenty-three benchmark functions of various types and dimensions. Then, the results are compared with the basic WOA, several variants of WOA and other swarm-based intelligent algorithms. The experimental results show that the proposed JSWOA algorithm with multi-mechanisms is superior to WOA and the other state-of-the-art algorithms in the competition, exhibiting remarkable advantages in the solution accuracy and convergence speed. It is also suitable for dealing with high-dimensional global optimizationproblems.
In this paper, a Surrogate-Assisted Grey Wolf optimization (SAGWO) algorithm for high-dimensional and computationally expensive problems is presented, where Radial Basis Function (RBF) is employed as the surrogate mod...
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In this paper, a Surrogate-Assisted Grey Wolf optimization (SAGWO) algorithm for high-dimensional and computationally expensive problems is presented, where Radial Basis Function (RBF) is employed as the surrogate model. SAGWO conducts the search in three phases, initial exploration, RBF-assisted meta-heuristic exploration, and knowledge mining on RBF. In the initial exploration, the Design of Experiments is carried out to generate a group of well-distributed samples based on which the original wolf pack and wolf leaders are sequentially identified to approximate the high-dimensional space roughly. The knowledge mining on RBF includes a global search that is carried out using the grey wolf optimization and a local search that is performed over a focused local region using a search strategy combining global and multi-start local exploration. In the proposed SAGWO, knowledge gained from the RBF model assists the generation of new wolf leaders in each cycle, and the positions of the wolf pack are iteratively changed following the wolf leaders, thus reaching balanced exploitation and exploration. The new SAGWO algorithm presents superior computation efficiency and robustness as demonstrated by comparison tests with ten representative global optimization algorithms on 30, 50 and 100 design variables.
Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capabilit...
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Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (RMOP) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other wellknown optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.
Continuous Estimation of Distribution Algorithms (EDAs) commonly use a Gaussian distribution to control the search process. For high-dimensional optimization problems, several practical issues arise when estimating a ...
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ISBN:
(纸本)9783319135632;9783319135625
Continuous Estimation of Distribution Algorithms (EDAs) commonly use a Gaussian distribution to control the search process. For high-dimensional optimization problems, several practical issues arise when estimating a large covariance matrix from the selected population. Recent work in continuous EDAs has aimed to address these issues. The Screening Estimation of Distribution Algorithm (sEDA) is one such algorithm which, uniquely, utilizes the objective function values obtained during the search. A sensitivity analysis technique is then used to reduce the rank of the covariance matrix, according to the estimated sensitivity of the fitness function to individual variables in the search space. In this paper we analyze sEDA and find that it does not scale well to very high-dimensionalproblems because it uses a large number of additional fitness function evaluations per generation. A modified version of the algorithm, named sEDA-lite is proposed which requires no additional fitness evaluations for sensitivity analysis. Experiments on a variety of artificial and real-world representative problems evaluate the performance of the algorithm compared with sEDA and EDA-MCC, a related, recently proposed algorithm.
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