Multi-factorial evolutionary algorithm based novel solution approach for multi-objective pollution-routing problem

Rauniyar, Amit; Nath, Rahul; Muhuri, Pranab K.

doi:10.1016/j.cie.2019.02.031

Cited by 58 publications

(26 citation statements)

References 28 publications

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“…The other problems such as pollution routing problem [111], [112], ship weather routing problem [113], urban freight transportation planning problem [114], many-objective dynamic route planning [115], VRP with demand responsive transport [116], [117], VRP with time windows [118], and other routing problems [119], [120], [121], [122], [123], [124] are considered under the category of VRPs.…”

Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

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A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

“…Rauniyar et al [111] incorporated a new paradigm of multifactorial optimization into NSGA-II to handle the multiobjective pollution routing problem. Experiments were performed based on benchmark task sets.…”

Section: C) Vehicle Routing Problemmentioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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“…MFEA [63] Pollution-routing problem (PRP) None [166] Package delivery problem (PDP) EEMTA [112] Team orienteering problem with time windows (TOPTW)…”

Section: Domain Problem Algorithmsmentioning

confidence: 99%

“…Rauniyar et al [166] put forward an MFEA based on NSGA-II to solve the pollutionrouting problem (PRP). The authors considered a PRP formulation with two conflicting objectives: minimization of fuel consumption, and minimization of total travel distance.…”

Section: Discrete Optimization Problemmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

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Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.

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“…In the literature, there exist a lot of works to apply MFEA to tackle real-world problems, such as complex supply chain network management [16], bi-level optimization problem [14], double-pole balancing problem [17], composites manufacturing problem [14,18], branch testing in software engineering [19], cloud computing service composition problem [20], pollution-routing problem [21], operational indices optimization of beneficiation process [22], and time series prediction problem [23].…”

Section: Related Work On Mtomentioning

confidence: 99%

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Wang¹,

Xu²,

Rong³

et al. 2019

IJCIS

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A B S T R A C TMulti-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization. MTO paradigm [6]. If the optimization tasks happen to bear some commonality or complementarity, then the inclusion of knowledge transfer often leads to significant performance improvements relative to conventional EAs alone.

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Multi-factorial evolutionary algorithm based novel solution approach for multi-objective pollution-routing problem

Cited by 58 publications

References 28 publications

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

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