Modified multi-verse optimizer for solving numerical optimization problems

Jui, Julakha Jahan and Mohd Ashraf, Ahmad and Muhammad Ikram, Mohd Rashid (2020) Modified multi-verse optimizer for solving numerical optimization problems. In: 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 20 June 2020 , Shah Alam, Selangor, Malaysia. pp. 81-86.. ISBN 978-1-7281-6133-4

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DOI/Official URL: DOI: 10.1109/I2CACIS49202.2020


The hybrid version of a multi-verse optimizer (MVO) namely the modified multi-verse optimizer (mMVO) is developed in this paper by modifying the position updating equation of MVO. Here two modifications are proposed in the standard MVO. Firstly, an average position selection mechanism is proposed for solving the local optima problem and secondly, the MVO algorithm is hybrid with another metaheuristics algorithm namely the Sine Cosine Algorithm (SCA) for better balancing the exploration and exploitation of standard MVO algorithm so that it can improve its searching capability. The proposed version of MVO has been evaluated on 23 well known benchmark functions namely unimodal, multimodal and fixed-dimension multimodal benchmark functions and the results are then verified with the standard MVO algorithm. Experimental results demonstrate that the proposed mMVO algorithm gives much better improvement than the standard MVO in the optimization problems in the sense of preventing local optima and increasing the search capability

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Optimization; multi-verse optimizer (MVO); sine cosine algorithm (SCA); benchmark functions; Benchmark testing: Optimization; Standards: Conferences: Intelligent systems: Wheels: Search problems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Faculty/Division: Institute of Postgraduate Studies
Depositing User: Ms. Ratna Wilis Haryati Mustapa
Date Deposited: 29 Jul 2020 02:49
Last Modified: 29 Mar 2021 08:49
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