Ensemble of many-objective evolutionary algorithms for many-objective problems

Zhou, Yue; Wang, Jiahai; Chen, Jian; Gao, Shangce; Teng, Luyao

doi:10.1007/s00500-015-1955-3

Cited by 28 publications

(6 citation statements)

References 60 publications

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“…Currently, there is wide range of many-objective evolutionary algorithms (MaOEAs) [4] exhibiting specific convergence and diversity properties. Zhou et al [56] proposed an ensemble of MaOEAs (denoted as EMaOEA) following the master-slave strategy. Each slave node executes an independent MaOEA.…”

Section: Algorithmic-level Parallelizationmentioning

confidence: 99%

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Falcón-Cardona

Gómez

Coello

et al. 2021

Swarm and Evolutionary Computation

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Section: Algorithmic-level Parallelizationmentioning

confidence: 99%

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Falcón-Cardona

Gómez

Coello

et al. 2021

Swarm and Evolutionary Computation

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“…The results showed that BSANCS was effective and feasible in enhancing the solution efficiency and exploration ability. The authors of the study suggested applying BSANCS on combinatorial optimisation problems [116,117], multi-objective optimisation [118,119], and neural network learning tasks [120,121].…”

Section: Bsancsmentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems.This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are twofold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.In artificial intelligence (AI), besides the existing traditional symbolic and statistical methods [1], computational intelligence (CI) has emerged as a recent research area [2]. According to [3], CI deals with collections of nature-inspired algorithms and approaches that in turn have been used to deal with complex practical problems that cannot be feasibly or effectively solved by traditional methods. CI comprises three subfields: fuzzy logic, evolutionary computation, and neural network.The concept of swarm intelligence (SI) is a part of evolutionary computation. It deals with artificial and natural systems that comprise several individuals and possess the ability of self-organisation and decentralised control.This concept, which was primarily initiated by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems, is used in AI [4]. Typically, the SI system comprises a collection of agents. The agents interact with their own environment or with each other. SI syst...

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“…Zhou et al [58]represented a group of MaOEAs for many objective matters. Exploratory findings on 80 benchmark issues showed that by combining the positives of distinctive MaOEAs into one arrangement, EMaOEA not only offers professionals with a related framework to recognize their range of problems, but can also request preferred execution over a single MaOEA.Zhou et al , [72] surveys the progress of MOEAs over the last few years, generally. It unwraps algorithmic systems, such as decay-based MOEAs (MOEA / Ds), memetic MOEAs, coevolutionary MOEAs, supervisors of choice and posterity propagation, MOEAs with explicit pursuit techniques, MOEAs for multimodal issues, limitation taking care of and MOEAs, computationally costly multiobjective streamlining issues (MOPs), dynamic MOPs, boisterous MOPs, combinatorial and discrete MOPs, benchmark issues, execution pointers, and its potential applications.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

2020

IJETER

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Information retrieval systems (IRSs) presentation is generally calculated using two special principles, accuracy and analysis. Exactness is the proportion of appropriate documents retrieved by the Information Retrieval System to the overall number of archives retrieved in the context of a client's inquiry; while review is the proportion of the quantity of pertinent reports recovered to the complete number of important reports for the client's question that are present in the narrative database. We present an investigation of two notable broadly useful multi-objective transformative algorithms, Challenges of Multi-objective Evolutionary Algorithms, Applications and Recent improvements in Multi-objective Evolutionary Algorithms all briefly. Further this examination talks about the diverse writing attempts to make the examination.

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Ensemble of many-objective evolutionary algorithms for many-objective problems

Cited by 28 publications

References 60 publications

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

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