Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems

Ghanem, Waheed Ali H. M.; Jantan, Aman

doi:10.1007/s00521-016-2665-1

Cited by 46 publications

(18 citation statements)

References 37 publications

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“…However, to improve the fitness value and convergence, a new solution can be updated when the fitness value is better than the previous one. Ghanem and Jantan [35] combined MBO with artificial bee colony (ABC) optimization to achieve global solution searching ability. To avoid the proposed algorithm falling into the local optimal solution, a modified butterfly adjusting operator is used as a mutation operator in ABC.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Kim

Chae

2019

Mathematics

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Facility layout problems (FLPs) are concerned with the non-overlapping arrangement of facilities. The objective of many FLP-based studies is to minimize the total material handling cost between facilities, which are considered as rectangular blocks of given space. However, it is important to integrate a layout design associated with continual material flow when the system uses circulating material handling equipment. The present study proposes approaches to solve the layout design and shortest single loop material handling path. Monarch butterfly optimization (MBO), a recently-announced meta-heuristic algorithm, is applied to determine the layout configuration. A loop construction method is proposed to construct a single loop material handling path for the given layout in every MBO iteration. A slicing tree structure (STS) is used to represent the layout configuration in solution form. A total of 11 instances are tested to evaluate the algorithm’s performance. The proposed approach generates solutions as intended within a reasonable amount of time.

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Section: Background and Literature Reviewmentioning

confidence: 99%

“…The remainders are set following the original parameter setting of [9]. Many studies using MBO [10][11][12]14,35] have adopted this same parameter setting because the original setting was based on the bio-inspired migration rate of the monarch butterfly.…”

Section: Experiments Informationmentioning

confidence: 99%

Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Kim

Chae

2019

Mathematics

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“…Ghanem and Jantan [19] combined ABC with elements from MBO to proposed a new hybrid metaheuristic algorithm named Hybrid ABC/MBO (HAM). The combined method used an updated butter y adjusting operator, considered to be a mutation operator, with the aim of sharing the information with the employee bees in ABC.…”

Section: Mbo Algorithmmentioning

confidence: 99%

Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

Wang

2019

Open Geosciences

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China has one of the highest rates of natural disasters in the world. In recent years, the Chinese government has placed a high value on improving emergency natural disaster relief. The goal of this research was to resolve a key issue for emergency natural disaster relief: the emergency vehicle routing problem (EmVRP) with relief materials in sudden disasters. First, we provided a description of the EmVRP, and de ned the boundary conditions. On this basis, we constructed an optimization model of EmVRP with relief materials in sudden disasters. To reach the best solution in the least amount of time, we proposed an enhanced monarch butter y optimization (EMBO) algorithm, incorporating two modi cations to the basic MBO: a self-adaptive strategy and a crossover operator. Finally, the EMBO algorithm was used to solve the EmVRP. Our experiments using two examples EmVRP with relief materials in a sudden-onset disaster proved the suitability of EMBO. In addition, an array of comparative studies showed that the proposed EMBO algorithm can achieve satisfactory solutions in less time than the basic MBO algorithm and seven other intelligent algorithms.

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“…In adding together it moreover includes a mutation operator in order towards increase the range of the population towards improves the investigate success of member selection procedure. This operator in the ENBA algorithm gives a new pair of optimizing of Lim1 and Lim2 parameters, depending on the recent work [25][26]. In this stage, if a random value is lower when compared to Lim1, then a result is randomly choose from the population as shown in Eq.…”

Section: Enhanced Bat Algorithm (Enba)mentioning

confidence: 99%

ISVS3CE: Incremental Support Vector Semi-Supervised Subspace Clustering Ensemble and ENhanced Bat Algorithm (ENBA) for High Dimensional Data Clustering

Karthika¹,

Kalaiselvi²

2019

IJRTE

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In the recent work, Incremental Soft Subspace Based Semi-Supervised Ensemble Clustering (IS4EC) framework was proposed which helps in detecting clusters in the dataset. IS4EC framework also increases the results of clustering by reducing the intra-cluster distance and increasing the inter-cluster distance with increased cluster quality. It cannot attain acceptable results while handling high dimensional data. However, decreasing the dimensional subspace becomes extremely difficult issue. In IS4EC framework, to choose the optimal ensemble members also extremely becomes challenging issue. In order to solve these issues of traditional cluster ensemble methods, first propose an Incremental Support vector Semi-Supervised Subspace Clustering Ensemble (ISVS3CE) framework which makes utilized of benefits of the random subspace algorithm and the Constraint Propagation (CP) algorithm. Here the centroid values were selected by using the Support Vector Machine (SVM) classifier. In the ISVS3CE framework, Incremental Ensemble Member Chosen (IEMC) process is performed by using the ENhanced Bat Algorithm (ENBA), and the normalized cut algorithm is introduced to perform high dimensional data clustering. The ISVS3CE framework is successful for solving high dimensional data issue, at the same time as the CP algorithm is valuable for incorporating the prior information. Results demonstrate that the proposed ISVS3CE framework performs well on datasets by means of very high dimensionality, and better than the traditional clustering ensemble methods

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Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems

Cited by 46 publications

References 37 publications

Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

ISVS3CE: Incremental Support Vector Semi-Supervised Subspace Clustering Ensemble and ENhanced Bat Algorithm (ENBA) for High Dimensional Data Clustering

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