Multifactorial PSO-FA Hybrid Algorithm for Multiple Car Design Benchmark

Xiao, Heng; Yokoya, Gen; Hatanaka, Toshiharu

doi:10.1109/smc.2019.8914649

Cited by 23 publications

(16 citation statements)

References 12 publications

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“…AdaMOMFDE [23], MFEA/D-DRA [128] Inter-domain path computation under domain uniqueness constraint (IDPC-DU) MFEA [71] Optimal power flow (OPF) problem MFEA [177] Electric power dispatch problem MO-MFO [178] Well location optimization problem AT-MFEA [116] Operation optimization of integrated energy system MO-MFEA-II [121] Car structure design optimization problem Multifactorial PSO-FA hybrid algorithm [91], TS+FM [ Evolutionary algorithms often lose their effectiveness and efficiency when applied to large-scale optimization problems. Feng et al [111] presented a primary trial of solving large-scale optimization (up to 2000 dimensions) via the evolutionary multi-task assisted random embedding method.…”

Section: Operational Indices Optimization Of Beneficiation (Oiob)mentioning

confidence: 99%

“…In the Mazda multiple car design benchmark problem, three kinds of cars (SUV, CDW, and C5H) with different sizes and body shapes need to be optimized simultaneously [183]. This MTO problem was solved by two distinct MTEC algorithms [91,95].…”

Section: Industrial Engineeringmentioning

confidence: 99%

“…Specifically, several typical genetic strategies include simulated binary crossover [18,79], ordered crossover (OX) [57,80], onepoint crossover [59,61], DE crossover [61], guided differential evolutionary crossover [81], partially mapped crossover (PMX) and two-point crossover (TPX) [71], Gaussian mutation [18], uniform mutation [61], swap mutation (SW) [57,80], polynomial mutation [53,79], DE mutation [61], mutation using the Powell search method [81], swap-change mutation [64], and one-point mutation [71]. The other EAs, differential evolution (DE) [82][83][84][85][86][87], particle swarm optimization (PSO) [85][86][87][88][89][90][91][92][93][94], artificial bee colony (ABC) [95], fireworks algorithm (FWA) [96], self-organized migrating algorithm (SOMA) [97], brain storm optimization (BSO) [98,99], Bat Algorithm (BA) [100], and genetic programming (GP) [61], are also utilized as fundamental algorithm for MTEC paradigms.…”

Section: Intro-population Reproductionmentioning

confidence: 99%

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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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Section: Operational Indices Optimization Of Beneficiation (Oiob)mentioning

confidence: 99%

Section: Industrial Engineeringmentioning

confidence: 99%

Section: Intro-population Reproductionmentioning

confidence: 99%

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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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“…Adaptive: [9,12,18,28,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70] [ 20,27,31,74,75,76,77,78,79,80,81,…”

Section: Staticmentioning

confidence: 99%

“…Main inspiration of that method is to have a more controlled implicit mating process among different tasks, favoring in this way the exploration and quantitative examination of synergies among the problems being solved. Also interesting is the approach introduced in [75] proposing a multifactorial particle swarm optimization -firefly algorithm hybrid technique. Main feature of this method is that individuals of the population can behave as a particle or a firefly, depending on the search performance.…”

Section: Implicit Knowledge Transfer Based Static Solversmentioning

confidence: 99%

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

Osaba¹,

Martinez²,

Ser³

2021

Preprint

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In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of Evolutionary Multitasking tackles multitask optimization scenarios by using as inspiration concepts drawn from Evolutionary Computation. The main purpose of this survey is to collect, organize and critically examine the abundant literature published so far in Evolutionary Multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can stimulate future efforts in this topic. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.

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COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking

Osaba

Ser

Yang

et al. 2020

Lecture Notes in Computer Science

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Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.

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Multifactorial PSO-FA Hybrid Algorithm for Multiple Car Design Benchmark

Cited by 23 publications

References 12 publications

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

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

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking

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