An improved differential harmony search algorithm for function optimization problems

Wang, Lin; Hu, Huanling; Liu, Rui; Zhou, Xiaojian

doi:10.1007/s00500-018-3139-4

Cited by 40 publications

(19 citation statements)

References 62 publications

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“…It was proven that the proposed approach achieve better results compared to other similar methods in terms of diversity measure, running time, and stego-image. Wang et al [37] proposed an improved differential harmony search (IDHS) method for numerical function optimization problems. The proposed IDHS balances the exploitation and exploration searching for the best solution using mutation strategies from the differential evolution (DE).…”

Section: Related Workmentioning

confidence: 99%

IWORMLF: Improved Invasive Weed Optimization With Random Mutation and Lévy Flight for Beam Pattern Optimizations of Linear and Circular Antenna Arrays

et al. 2020

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The beam pattern synthesis of antenna arrays is one of the most significant optimization problems in the electromagnetics community, and it affects the performance of wireless communication systems. In this article, we formulate the beam pattern optimization problems, and propose an improved invasive weed optimization with random mutation and Lévy flight (IWORMLF) algorithm to synthesize the beam patterns of linear antenna arrays (LAAs) and circular antenna arrays (CAAs). IWORMLF introduces the random mutation operator and the Lévy flight mechanism to enhance the efficiency and to balance the exploration and exploitation of the algorithm for optimization problems. Several simulations are conducted to evaluate the performance of the proposed approach. First, IWORMLF is tested by a variety of benchmark functions, and the results indicate that it achieves the best performance in most of the functions compared to some other algorithms. Second, IWORMLF is utilized for the maximum sidelobe level (SLL) reduction and the joint maximum SLL and mainlobe beamwidth reductions. The results show that it achieves better performances in terms of the convergence rate, stability, and accuracy compared to some evolutionary algorithms for these optimization cases. Third, the efficiencies of the improved factors are verified. Finally, electromagnetism simulations are conducted to evaluate the performance of IWORMLF for the beam pattern optimizations with considering mutual coupling.INDEX TERMS Antenna array, beam pattern, sidelobe level, invasive weed optimization.

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Section: Related Workmentioning

confidence: 99%

IWORMLF: Improved Invasive Weed Optimization With Random Mutation and Lévy Flight for Beam Pattern Optimizations of Linear and Circular Antenna Arrays

et al. 2020

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“…The improvisation stage may cause a temporary stay due to local optimization, which may affect the algorithm's convergence speed and accuracy. Inspired by references [28][29][30][31][32], an adaptive harmony search utilizing global optimal (AGOHS) is proposed based on the following aspects. First aims at the adjustment method of the bandwidth of the HS algorithm in the improvisation stage.…”

Section: Adaptive Harmony Search Utilizing Global Optimalmentioning

confidence: 99%

“…To test the effectiveness of AGOHS, this paper uses the 13 classic test functions mentioned in the reference [33][34][35] as the benchmark function. Compared with three improved HS algorithms, such as IDHS [30], HSDM [31], and ID-HS-LDD [32], the optimization results are compared and analyzed under the condition that each of the 13 test function optimization operations is run 50 times separately. Among them, the functions F1-F11 are compared after 5000 iterations in 10 and 30 dimensions respectively, and the functions F12 and F13 are compared after 5000 iterations in VOLUME XX, 2017 a fixed dimension.…”

Section: Experiments and Related Analysismentioning

confidence: 99%

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Zhou

et al. 2020

IEEE Access

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Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.

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“…In addition, the HMCR and PAR are tuned automatically during the search process based on the newly generated solutions. Another enhancement was introduced by Wang et al [75] with an improved differential harmony search algorithm. The algorithm uses two differential evolution mutation operators to improve the algorithm exploitation.…”

Section: Introductionmentioning

confidence: 99%

Best Polynomial Harmony Search with Best β-Hill Climbing Algorithm

Doush

Santos

2020

Journal of Intelligent Systems

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AbstractHarmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory consideration, replace the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β-hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β-hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β-hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHSβ–HC and the second one is called Imp. PHSβ–HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHSβ–HC) and 3rd (PHSβ–HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHSβ–HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHSβ–HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHSβ–HC algorithm produces the best results for three test functions, and the proposed Imp. PHSβ–HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future.

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An improved differential harmony search algorithm for function optimization problems

Cited by 40 publications

References 62 publications

IWORMLF: Improved Invasive Weed Optimization With Random Mutation and Lévy Flight for Beam Pattern Optimizations of Linear and Circular Antenna Arrays

IWORMLF: Improved Invasive Weed Optimization With Random Mutation and Lévy Flight for Beam Pattern Optimizations of Linear and Circular Antenna Arrays

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Best Polynomial Harmony Search with Best β-Hill Climbing Algorithm

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