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dc.contributor.authorJalali, Seyed Mohammad Jafar
dc.contributor.authorAhmadian, Sajad
dc.contributor.authorKhosravi, Abbas
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorMahmoudi, Mohammad Reza
dc.contributor.authorNahavandi, Saeid
dc.date.accessioned2020-11-06T05:40:19Z
dc.date.available2020-11-06T05:40:19Z
dc.date.issued2020
dc.identifier.issn2214-4366
dc.identifier.doi10.1016/j.cogsys.2020.04.001
dc.identifier.urihttp://hdl.handle.net/10072/399041
dc.description.abstractThe field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained and optimized using four advanced bio-inspired evolutionary algorithms (EA). The algorithms are Multi-Verse Optimizer (MVO), Moth-flame optimization (MFO), Cuckoo Search (CS) and Particle Swarm Optimization (PSO). Each algorithm is equipped with two operators: evolutionary population dynamics and mutation, which impact on exploration and exploitation. Optimized MLPs are then used for the navigation of an autonomous robot. Accuracy and area under the curve metrics are used for the evaluation and comparison metrics. Moreover, two well-regarded gradient descent algorithms including Back propagation (BP) and Levenberg Marquardt (LM) are utilized to validate the results obtained by evolutionary-based MLP trainers. It is observed that MLPs developed using MFO are the most robust ones among MLPs trained using other evolutionary and gradient descent algorithms.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom35
dc.relation.ispartofpageto43
dc.relation.ispartofjournalCognitive Systems Research
dc.relation.ispartofvolume62
dc.subject.fieldofresearchPsychology
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchPhilosophy
dc.subject.fieldofresearchBiological psychology
dc.subject.fieldofresearchcode52
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode5003
dc.subject.fieldofresearchcode5202
dc.subject.keywordsScience & Technology
dc.subject.keywordsSocial Sciences
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.titleNeuroevolution-based autonomous robot navigation: A comparative study
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationJalali, SMJ; Ahmadian, S; Khosravi, A; Mirjalili, S; Mahmoudi, MR; Nahavandi, S, Neuroevolution-based autonomous robot navigation: A comparative study, Cognitive Systems Research, 2020, 62, pp. 35-43
dc.date.updated2020-11-06T05:39:08Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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