Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

Liu, Chao; Niu, Peifeng; Li, Guoqiang; Ma, Yunpeng; Zhang, Weiping; Chen, Ke

doi:10.1007/s10845-015-1164-z

Cited by 36 publications

(17 citation statements)

References 35 publications

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“…wherein, X w is the current latest position, O max and O min are the maximum and minimum values of the search range among the frog subgroups, respectively [114]. The iterative update step above is continuously repeated until the maximum number of iterations set in the previously set subgroup is satisfied.…”

Section: E Shuffled Frog Leaping Algorithmmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Section: E Shuffled Frog Leaping Algorithmmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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“…The SFLA, developed by Amiri et al, is a population‐based metaheuristic method that uses mathematical functions to find an optimized solution. In this algorithm, a population of possible solutions is determined based on regulation of frogs . The third optimization algorithm is ACO and was developed by Dorigo et al This algorithm is a metaheuristic optimization algorithm inspired by the behavior of ants' colonies in nature.…”

Section: Optimization Techniquesmentioning

confidence: 99%

“…In this algorithm, a population of possible solutions is determined based on regulation of frogs. 31 The third optimization algorithm is ACO and was developed by Dorigo et al 32 This algorithm is a metaheuristic optimization algorithm inspired by the behavior of ants' colonies in nature. ACO was first introduced to solve discrete optimization problems and later extended to both discrete and continuous variables.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Applying multiple decomposition methods and optimization techniques for achieving optimal cost in mixed materials heat exchanger networks

Karimi

Ahmadi‐Danesh‐Ashtiani

Aghanajafi

2019

Int J Energy Res

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Summary The optimization of the total annual cost in heat exchanger networks has been one of the overarching goals when synthesizing these networks. Several methodologies and techniques have been developed to achieve optimal costs in mixed material heat exchanger networks. This paper demonstrates the application of two decomposition methodologies (total decomposition and partial decomposition) for typical cost rules. The objective function was defined as the optimization and minimization of the total annual cost in mixed materials heat exchanger network. Three optimization algorithms, hybrid genetic‐particle swarm optimization (GA‐PSO), shuffled frog leaping algorithm (SFLA) techniques, and ant colony optimization (ACO), were used to further optimize the total cost in mixed materials heat exchanger network. The results indicate that the total annual cost in partial decomposition method was smaller than that in full integration method and total decomposition method. The reduction of the total annual cost was about 27% for GA‐PSO algorithm, 24% for SFLA and 10% for ACO relative to the results reported in this work. In partial decomposition method, at least one mixed material of heat exchanger was used to reduce the hot and cold utility for decreasing the total annual cost. Partial decomposition method resulted in the highest reduction of the total annual cost compared with other methods. Percentage of difference of the total annual cost were 0.36%, 1.92%, and 5.05% for full integration, total decomposition, and partial decomposition methods, respectively, in comparison with the previous studies. Results have been compared with the results of other studies to demonstrate the accuracy of the applied algorithms.

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“…It has been shown to be competitive with PSO, but the SFLA is good at exploration but poor at exploitation and easily gets trap in local optima when solving partial complex multimodal problems. Meanwhile, its convergence speed is 2 Complexity slower [9]. Tan and Zhu [10] designed a fireworks algorithm (FWA) for the global optimization of complex functions.…”

Section: Introductionmentioning

confidence: 99%

“…Then, considering that MBO converges very slowly, a perturbation operator strategy can be used to ensure the diversity of monarch butterfly against the premature convergence. For example, Liu et al [9] designed a perturbation operator strategy in a convergence state to help the best frog to jump out of possible local optima to further increase the performance of SFLA. Wang et al [56] proposed an improved FOA using swarm collaboration and random perturbation strategy to enhance the performance.…”

Section: Introductionmentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

Cited by 36 publications

References 35 publications

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Applying multiple decomposition methods and optimization techniques for achieving optimal cost in mixed materials heat exchanger networks

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

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