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window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":117923417,"created_at":"2024-04-23T00:52:10.580-07:00","from_world_paper_id":253317261,"updated_at":"2024-11-26T02:47:51.893-08:00","_data":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"A multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to quickly obtain a fixed size set approximating the complete Pareto-front. It adapts ideas from different multi-objective optimization evolutionary algorithms, but also incorporates new devices. In particular, the search in the space is carried out on promising areas (hyperspheres) determined by a radius value, which decreases as the optimization procedure evolves. This mechanism helps to maintain a balance between exploration and exploitation of the search space. Additionally, a new local search method which accelerates the convergence of the population towards the optimal Pareto-front, has been incorporated. It is an extension of the local optimizer SASS and improves a given solution along a search direction (no gradient information is used). Finally, a termination criteria has also been proposed, which stops the algorithm if during three consecutive iterations the changes experimented in the candidate Pareto-front are negligible (in terms of the objective function values). To know how far two sets are from each other, a modification of the well-known Hausdorff distance is proposed. In order to analyze the algorithm performance, it has been compared to the reference algorithms NSGA-II and SPEA2 and the state-of-the-art","publication_date":"2016,,","publication_name":"Mathematical Methods of Operations Research","grobid_abstract_attachment_id":"113666616"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"FEMOEA: a fast and efficient multi-objective evolutionary algorithm","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [294358709]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div 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The aim is to find or approximate the Pareto optimal set in a reasonable time. In this work, we present a new approach that divides the objective search-space into different partitions and assigns each processor its corresponding partition. Each processor will try to find the set of solutions for its partition only. The sub-Pareto fronts will be combined later and the parallelisation approach is based on a mutli-start approach by having independent algorithm on every processor with its own starting points. Experimental results on well known test cases showed that the proposed method outperformed several state-of-the-art evolutionary algorithms regarding convergence to the true Pareto front and gave very competitive results when considering the hypervolume metric. 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