Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$ p -xylene oxidation process

Fan, Qinqin; Yan, Xuefeng

doi:10.1007/s10845-015-1087-8

Cited by 16 publications

(2 citation statements)

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“…P 0 1 shows initial population which contains all ϕ initial solutions. Define t = 0 and the value of ϕ. Allocate t s = 0.2 * ϕ (see Fan et al (2017) [30] for more information). A mutation operation pool (Q t ) with ϕ/2 operations is obtained from D/r d /1 and D/b s /1 models, respectively.…”

Section: Multi-objective Differential Evolutionmentioning

confidence: 99%

Fusion of multi-modality biomedical images using deep neural networks

Gupta¹,

Kumar

Gupta³

et al. 2022

Soft Comput

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With the recent advancement in the medical diagnostic tools, multi-modality medical images are extensively utilized as a lifesaving tool. An efficient fusion of medical images can improve the performance of various medical diagnostic tools. But, gathering of all modalities for a given patient is defined as an ill-posed problem as medical images suffer from poor visibility and frequent patient dropout. Therefore, in this paper, a novel image fusion model is designed to fuse multi-modality medical images. The proposed model model suffers from hyper-parameters tuning issue, therefore, a multi-objective differential evolution (MDE) is used to optimize the initial parameters of the proposed model. The fusion factor and edge strength metrics are utilized to form a multi-objective fitness function. The performance of the proposed model is validated by comparing the proposed model models with nine competitive models over fifteen benchmark images. Performance analysis reveal that the proposed model outperforms the competitive fusion models.

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Section: Multi-objective Differential Evolutionmentioning

confidence: 99%

Fusion of multi-modality biomedical images using deep neural networks

Gupta¹,

Kumar

Gupta³

et al. 2022

Soft Comput

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show abstract

“…Generally, in P 0 , each ĵ th component (ĵ = 1, 2, 3, ..., D) of the i th individuals (i = 1, 2, 3, ..., ŃҎ ) is obtained as per Eq. (2) [18];…”

Section: Initializationmentioning

confidence: 99%

Multi-Objective Differential Evolution Algorithm with a New Environmental Parameter based Mutation for Solving Optimization Problems

et al. 2021

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Simultaneous optimization of two or more objectives is an instance of multi-objective optimization (MOO). However, for most of the Multi-Objective Problems (MOPs), no single solution can optimize all the objective functions simultaneously. Evolutionary algorithms are a type of optimization algorithm used for constructing a well-distributed optimal front more quickly than a much more efficient approach. One of the most commonly used algorithms in this regard is differential evolution (DE). To solve the problem of non-dominated sorting for Multi-Objective DE (MODE), an approach is proposed that reduces the time complexity. DE is commonly recognized as one of the best methods for solving the MOPs. Several versions of DEs have been proposed for MOPs. This article proposes a novel mutation technique, named Environmental Parameter-based Multi-Objective Differential Evolution (EP-MODE) that acquires two additional environmental parameters to preserve diversity and accelerate the convergence. The proposed approach also reduces the time complexity of non-dominated sorting for Multi-Objective DE (MODE). The proposed mutation's performance has been evaluated on DTLZ series and ZDT series benchmark test functions on the Pareto-optimal front and compared with existing multi-objective algorithms (MODE and MODE-RMO). The solution comparatively verifies that the proposed EP-MODE outperforms most of the MODEs for lower dimension functions and higher functions. HIGHLIGHTS New environmental parameters-based mutation has been introduced in a differential evolution algorithm to solve multi-objective optimization problems (EP-mode) The addition of two additional environmental parameters to EP-mode has been found to be appropriate for maintaining diversity while also accelerating the convergence rate EP-MODE evaluated on bi-objective (DTLZ series) and tri-objective (ZDT series) benchmark test functions on the Pareto-optimal front and found EP-MODE's efficiency is higher than that of the MODE algorithm and the MODE algorithm with a ranking-based mutation operator GRAPHICAL ABSTRACT

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