Moth-Flame Optimization Algorithm: Theory, Literature Review, and Application in Optimal Nonlinear Feedback Control Design

Mehne, Hamed Hashemi; Mirjalili, Seyedali

doi:10.1007/978-3-030-12127-3_9

Cited by 27 publications

(25 citation statements)

References 77 publications

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“…To evaluate the performance of the proposed GMBO, the two latest algorithms, namely, moth search (MS) [67] and moth-flame optimization (MFO) [68], were especially selected to compare with From Figure 19, the curves of GMBO and MBO were almost coincident before 6 seconds, but afterward, GMBO converged rapidly to a better value as compared to the others. From Figure 20, it is indeed interesting to note that MBO has a weak advantage in the average values as compared to GMBO.…”

Section: The Comparisons Of the Gmbo And The Latest Algorithmsmentioning

confidence: 99%

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A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Feng

Wang

2019

Mathematics

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As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem (0-1 KP) has received considerable attention among the relevant researchers. The monarch butterfly optimization (MBO) is a recent metaheuristic algorithm inspired by the migration behavior of monarch butterflies. The original MBO is proposed to solve continuous optimization problems. This paper presents a novel monarch butterfly optimization with a global position updating operator (GMBO), which can address 0-1 KP known as an NP-complete problem. The global position updating operator is incorporated to help all the monarch butterflies rapidly move towards the global best position. Moreover, a dichotomy encoding scheme is adopted to represent monarch butterflies for solving 0-1 KP. In addition, a specific two-stage repair operator is used to repair the infeasible solutions and further optimize the feasible solutions. Finally, Orthogonal Design (OD) is employed in order to find the most suitable parameters. Two sets of low-dimensional 0-1 KP instances and three kinds of 15 high-dimensional 0-1 KP instances are used to verify the ability of the proposed GMBO. An extensive comparative study of GMBO with five classical and two state-of-the-art algorithms is carried out. The experimental results clearly indicate that GMBO can achieve better solutions on almost all the 0-1 KP instances and significantly outperforms the rest.

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Section: The Comparisons Of the Gmbo And The Latest Algorithmsmentioning

confidence: 99%

Section: The Comparisons Of the Gmbo And The Latest Algorithmsmentioning

confidence: 99%

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Feng

Wang

2019

Mathematics

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“…Moth-Flame Optimization (MFO) [16] is a new swarm intelligence bionic algorithm that taken the inspiration from natural moth behavior. Due to its excellent performance, the algorithm has been widely used in engineering fields [17], e.g., a confined aquifer parameter inversion, Muskingum model parameter optimization [18], network flow prediction [19], and power system optimal power flow calculation [20]. Moreover, image segmentation is a key step in image analyzing and processing that transforms the original image into a more abstract and compact form, which makes it possible for higher-level image analysis [21].…”

Section: Introductionmentioning

confidence: 99%

A Scheme of Color Image Multithreshold Segmentation Based on Improved Moth-Flame Algorithm

et al. 2020

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A recently developed swarm intelligence algorithm by studying the natural moth's biological behavior is called Moth-Flame Optimization (MFO). The advantages of MFO conclude a simple structure and a robust selection capability. Still, it is easy to be trapped falling into optimal local, and slow search converges. This study suggests a new process improving MFO by hybridizing Lévy flight and logarithmic functions for its formula of flame updating to enhance the optimization performance of the algorithm. In the experimental section, a set of benchmark functions of CEC2013 and the multithreshold image segmentation are used to evaluate the proposed method performance. Compared results of the proposed methods with the different algorithms in the same condition scenarios show that the suggested approach provides better results than the various algorithms in the competitions.

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“…As the task scheduling is considered as a nondeterministic polynomial problem, heuristic algorithms are used in addressing this problem. The task scheduling in the presented paper is provided by using the moth‐flame optimization (MFO) algorithm . The main idea of the proposed method is to assign an optimal set of tasks to fog nodes to meet the satisfaction of QoS constraints in such a way that the total execution time (TET) of tasks is minimized.…”

Section: Introductionmentioning

confidence: 99%

“…The task scheduling in the presented paper is provided by using the moth-flame optimization (MFO) algorithm. 10 The main idea of the proposed method is to assign an optimal set of tasks to fog nodes to meet the satisfaction of QoS constraints in such a way that the total execution time (TET) of tasks is minimized. In this study, we defined the objective function as minimization of TET, which is calculated according to summation of task execution time and task transfer time metrics.…”

Section: Introductionmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

124

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Moth-Flame Optimization Algorithm: Theory, Literature Review, and Application in Optimal Nonlinear Feedback Control Design

Cited by 27 publications

References 77 publications

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

A Scheme of Color Image Multithreshold Segmentation Based on Improved Moth-Flame Algorithm

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

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