Optimization of problems with multiple objectives using the multi-verse optimization algorithm

Mirjalili, Seyedali; Jangir, Pradeep; Mirjalili, Seyedeh Zahra; Saremi, Shahrzad; Trivedi, Indrajit N.

doi:10.1016/j.knosys.2017.07.018

Cited by 273 publications

(93 citation statements)

References 60 publications

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“…Many recent studies have used this algorithm in order to solve various problems in different applications. Due to the wide range of applications and their needs, other variants of MVO such as Binary Multi-verse optimizer [41] and Multi-Objective Multi-Verse Optimizer (MOMVO) [42] have been presented. This metaheuristic algorithm inspired by famous theory called Multi-Verse theory.…”

Section: Multi-verse Optimizer (Mvo)mentioning

confidence: 99%

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Tabrizchi

2020

SN Appl. Sci.

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Breast cancer is among the most common cancers women got, which can be effectively cured providing that it is diagnosed at the early stages. In the current study, we attempted to classify breast cancer into two groups of malignant and benign by proposing a new ensemble learning method using Multi-Verse Optimizer (MVO) and Gradient Boosting Decision Tree (GBDT). Moreover, the prediction rate of GBDT has been shown to be desirable, its efficiency and classification accuracy are significantly dependent on feature selection and parameter setting. Based on the MVO, we attempted to propose an efficient approach to optimize feature selection and GBDT's parameters at the same time. In other words, the MVO algorithm is able to play the role of a tuner to set the GBDT's main parameters and optimize feature selection results. To implement and test the proposed approach, standard criteria (i.e. accuracy, sensitivity, specificity, etc.) was used for performance evaluation. Also, the datasets of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer were considered for this purpose. Comparing the results of GBDT-MVO model with other proposed models demonstrated that this model is more precise and has considerably lower variance in the case of a breast cancer diagnosis.

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Section: Multi-verse Optimizer (Mvo)mentioning

confidence: 99%

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Tabrizchi

2020

SN Appl. Sci.

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“…The Multi-Verse Optimizer (MVO) [35] is a novel heuristic algorithm proposed by Professor Mirjalili in 2015, which divides the search process into two aspects: exploration and development. There is always high possibility of moving objects from a universe with high inflation rate to a universe with low inflation rate.…”

Section: The Proposed Imomvo Algorithmmentioning

confidence: 99%

Multi‐Objective Reentrant Hybrid Flowshop Scheduling with Machines Turning on and off Control Strategy Using Improved Multi‐Verse Optimizer Algorithm

Geng

Cao

et al. 2019

Mathematical Problems in Engineering

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This paper focuses on the multi-objective optimization of the reentrant hybrid flowshop scheduling problem (RHFSP) with machines turning on and off control strategy. RHFSP exhibits significance in many industrial applications, but scheduling with both energy consumption consideration and reentrant concept is relatively unexplored at present. In this study, an improved Multi-Objective Multi-Verse Optimizer (IMOMVO) algorithm is proposed to optimize the RHFSP with objectives of makespan, maximum tardiness, and idle energy consumption. To solve the proposed model more effectively, a series of improved operations are carried out, including population initialization based on Latin hypercube sampling (LHS), individual position updating based on Lévy flight, and chaotic local search based on logical self-mapping. In addition, a right-shift procedure is used to adjust the start time of operations aiming to minimize the idle energy consumption without changing the makespan. Then, Taguchi method is utilized to study the influence of different parameter settings on the scheduling results of the IMOMVO algorithm. Finally, the performance of the proposed IMOMVO algorithm is evaluated by comparing it with MOMVO, MOPSO, MOALO, and NSGA-II on the same benchmark set. The results show that IMOMVO algorithm can solve the RHFSP with machines turning on and off control strategy effectively, and in terms of convergence and diversity of non-dominated solutions, IMOMVO is obviously superior to other algorithms. However, the distribution level of the five algorithms has little difference. Meanwhile, by turning on and off the machine properly, the useless energy consumption in the production process can be reduced effectively.

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“…There are many ways to solve multi-objective optimization problems. The most common approach is to transform the model into a single-objective problem [22][23][24]. In our model, the objective function TN has a completely different structure from that of TP and TS, making it difficult to find a combinatory solution.…”

Section: Hybrid Pso-ga Based On Directed Elite Searchmentioning

confidence: 99%

Multi-objective Decision-Making of New Retailing Terminals Based on Particle Swarm Optimization and Genetic Algorithm

Wu¹

2019

JESA

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This paper aims to solve the multi-objective decision-making for the optimal configuration of multi-type new retailing terminals. First, the distribution of consumer demand for convenience stores (CSs) and unmanned retail terminals (URTs) was investigated in different scenes. Then, the author set up an optimization model for the configuration of multi-type terminals, aiming to maximize the daily mean profit of the retailer, maximize consumer satisfaction in multiple dimensions, and minimize the number of terminals. To solve the model, the genetic algorithm (GA) and particle swarm optimization (PSO) were combined into a hybrid algorithm, based on elite recombination and directed optimization (directed elite search). The proposed model and algorithm were proved valid through empirical analysis. The results show that the retailer can achieve better profit and consumer satisfaction by deploying multiple types of retail terminals, and arranging different terminals at different scenes; moreover, the three optimization objectives can be achieved by improving the attraction of terminals, cautiously analyzing consumer rationality and properly setting the courier fee. The research findings lay theoretical basis for terminal configuration in new retailing and provide an important guide for new retailing operations.

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Optimization of problems with multiple objectives using the multi-verse optimization algorithm

Cited by 273 publications

References 60 publications

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Multi‐Objective Reentrant Hybrid Flowshop Scheduling with Machines Turning on and off Control Strategy Using Improved Multi‐Verse Optimizer Algorithm

Multi-objective Decision-Making of New Retailing Terminals Based on Particle Swarm Optimization and Genetic Algorithm

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