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Multi-objective complex mathematical models need to be solved by meta-heuristic algorithms in such a way that Pareto-optimal solutions are obtained; therefore, a new algorithm is presented in this paper for solving such models. The process of the interior search algorithm (ISA) is based on principles of interior design and decoration. This algorithm divides all elements, except the most suitable one, into two groups. In the first group, which is called the artistic composition group, algorithm changes the composition of elements to achieve a more desirable view. In the second group, which is called the mirror group, the algorithm places a mirror between the group elements and the most suitable element to find a better view. This paper uses the principles of the ISA in conjunction with the concepts of the non-dominated sorting and crowding distance to present the proposed MOISA, which is capable of obtaining near-optimal non-dominated solutions from solution space and identifying accurate Pareto fronts. To evaluate the performance of the foregoing algorithm, the related results of solving six models and a maximal covering location-allocation model are compared with several standard multi-objective evolutionary algorithms in terms of different metrics. This comparison shows that the results of the proposed MOISA are better than those obtained from other tested algorithms. Based on the solved numerical examples, the algorithm presented in this paper has many advantages over existing algorithms.<\/jats:p>","DOI":"10.3233\/jifs-172005","type":"journal-article","created":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T19:31:24Z","timestamp":1532719884000},"page":"3307-3319","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-objective interior search algorithm for optimization: A new multi-objective meta-heuristic algorithm"],"prefix":"10.1177","volume":"35","author":[{"given":"Navid","family":"Torabi","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"}]},{"given":"Reza","family":"Tavakkoli-Moghaddam","sequence":"additional","affiliation":[{"name":"School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran"},{"name":"Arts et M\u00e9tiers Paris Tech, LCFC, Metz, France"}]},{"given":"Esmaiel","family":"Najafi","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"}]},{"given":"Farhad","family":"Hosseinzadeh Lotfi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran"}]}],"member":"179","published-online":{"date-parts":[[2018,7,24]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"416","volume-title":"Proceedings of the fifth International Conference on Genetic Algorithms","author":"Fonseca C.M.","year":"1993","unstructured":"FonsecaC.M. and FlemingP.J., Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization, Proceedings of the fifth International Conference on Genetic Algorithms, Morgan Kauffman, San Mateo, CA, 1993, 416\u2013423."},{"key":"e_1_3_1_3_2","volume-title":"Multi-objective Optimization Using Evolutionary Algorithms","author":"Kalyanmoy D.","year":"2001","unstructured":"KalyanmoyD., Multi-objective Optimization Using Evolutionary Algorithms, Willy, 2001."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17144-4_1"},{"key":"e_1_3_1_5_2","first-page":"351","volume-title":"Proceedings of the ACM Symposium on Applied Computing","author":"Veldhuizen D.A.","year":"1999","unstructured":"VeldhuizenD.A., LamontG.B., Multiobjective evolutionary algorithm test suites, In: CarrollJ., HaddadH., OenheimD., BryantB. and LamontG.B., Eds., Proceedings of the ACM Symposium on Applied Computing, Saint Antonio, TX, 1999, pp. 351\u2013357."},{"key":"e_1_3_1_6_2","volume-title":"Multiobjective Programming and Planning","author":"Cohon J.L.","year":"1978","unstructured":"CohonJ.L., Multiobjective Programming and Planning, Academic Press, New York, 1978."},{"key":"e_1_3_1_7_2","volume-title":"Introduction to Operational Research","author":"Hillier F.","year":"1995","unstructured":"HillierF., LibermanG., Introduction to Operational Research, 6th Ed., MacGraw Hill, USA, 1995.","edition":"6"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1142\/5712"},{"key":"e_1_3_1_9_2","volume-title":"Multiobjective Optimization Using Evolutionary Algorithms","author":"Deb K.","year":"2001","unstructured":"DebK., Multiobjective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Chichester, UK, 2001."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/0-306-48041-7_7"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218213002001039"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_3_1_13_2","first-page":"99","volume-title":"Introduction to Genetic Algorithms","author":"Sivanandam S.N.","year":"2008","unstructured":"SivanandamS.N., DeepaS.N., Introduction to Genetic Algorithms, Springer, Berlin, Germany, 2008, pp. 99\u2013104."},{"key":"e_1_3_1_14_2","volume-title":"Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab","author":"Sumathi S.","year":"2008","unstructured":"SumathiS., HamsapriyaT., SurekhaP., Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab, Springer, Berlin, 2008."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1162\/evco.1994.2.3.221"},{"key":"e_1_3_1_16_2","first-page":"82","volume-title":"Proceedings of the First IEEE Conference on Evolutionary Computation","author":"Horn J.","year":"1994","unstructured":"HornJ., NafploitisN., GoldberD.E., A niched Pareto genetic algorithm for multiobjective optimization, in MichalewiczZ., Ed. 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