{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T11:21:02Z","timestamp":1774437662420,"version":"3.50.1"},"reference-count":39,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPCC"],"published-print":{"date-parts":[[2023,5,22]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>In this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE\/best\/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>To test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>In this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijpcc-08-2020-0101","type":"journal-article","created":{"date-parts":[[2021,9,11]],"date-time":"2021-09-11T02:37:25Z","timestamp":1631327845000},"page":"343-363","source":"Crossref","is-referenced-by-count":1,"title":["QoS aware web service selection using orthogonal array learning on fruit fly optimization approach"],"prefix":"10.1108","volume":"19","author":[{"given":"Manik","family":"Chandra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajdeep","family":"Niyogi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"key2024081912184054600_ref001","article-title":"Investigating web services on the world wide web","year":"2008"},{"key":"key2024081912184054600_ref002","first-page":"123","volume-title":"Web Services, Data-Centric Systems and Applications","year":"2004"},{"key":"key2024081912184054600_ref003","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.asoc.2016.01.019","article-title":"Opposition chaotic fitness mutation based adaptive inertia weight bpso for featureselection in text clustering","volume":"43","year":"2016","journal-title":"Applied Soft Computing"},{"issue":"2","key":"key2024081912184054600_ref004","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11047-008-9098-4","article-title":"A survey on metaheuristics for stochastic combinatorial optimization","volume":"8","year":"2009","journal-title":"Natural Computing"},{"key":"key2024081912184054600_ref005","first-page":"51","article-title":"A survey on services composition languages and models","volume-title":"In International Workshop on Web Services\u2013Modeling and Testing(WS-MaTe2006)","year":"2006"},{"key":"key2024081912184054600_ref006","article-title":"A lightweight approach for qos-aware service composition","year":"2004"},{"key":"key2024081912184054600_ref007","first-page":"88673","article-title":"Webservice selection using modified artificial bee colony algorithm","volume":"7","year":"2019","journal-title":"IEEEAccess"},{"key":"key2024081912184054600_ref008","first-page":"93","article-title":"Optimizing the semantic web service composition process using cuckoo search","volume-title":"In Intelligent Distributed Computing V","year":"2011"},{"issue":"1","key":"key2024081912184054600_ref009","first-page":"4","article-title":"Differential evolution: a survey of the state-of-the-art","volume":"15","year":"2010","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"key2024081912184054600_ref010","first-page":"1470","article-title":"Ant colony optimization: a new Meta-heuristic","volume":"2","year":"1999","journal-title":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. 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