A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem
This paper presents a novel hybrid genetic algorithm (GA)-particle swarm optimization (PSO) approach for reliability redundancy allocation problem (RRAP) in series, series–parallel, and complex (bridge) systems. The proposed approach maximizes overall system reliability while minimizing system cost,...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2013-09, Vol.68 (1-4), p.317-338 |
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| container_end_page | 338 |
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| container_title | International journal of advanced manufacturing technology |
| container_volume | 68 |
| creator | Sheikhalishahi, M. Ebrahimipour, V. Shiri, H. Zaman, H. Jeihoonian, M. |
| description | This paper presents a novel hybrid genetic algorithm (GA)-particle swarm optimization (PSO) approach for reliability redundancy allocation problem (RRAP) in series, series–parallel, and complex (bridge) systems. The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. The proposed approach could be helpful for engineers and managers to better understand their system reliability and performance, and also to reach a better configuration. |
| doi_str_mv | 10.1007/s00170-013-4730-6 |
| format | Article |
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The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. 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The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. The proposed approach could be helpful for engineers and managers to better understand their system reliability and performance, and also to reach a better configuration.</description><subject>CAE) and Design</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Computing time</subject><subject>Engineering</subject><subject>Genetic algorithms</subject><subject>Industrial and Production Engineering</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Original Article</subject><subject>Particle swarm optimization</subject><subject>Redundancy</subject><subject>Reliability engineering</subject><subject>System reliability</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kMFKAzEURYMoWKsf4C7gOvqSzCTpshStQqGCCu5CJpOxKdOZMZkuxpX_4B_6JaaM4MrVW9xz74OD0CWFawogbyIAlUCAcpJJDkQcoQnNOCccaH6MJsCEIlwKdYrOYtwmWlChJuh1jjdDEXyJl_Pvz6_HpzU2XRdaYze4agMOrvam8LXvB9x2vd_5D9P7tsG-SVm5b0rT2AGbum7tGKRyUbvdOTqpTB3dxe-dope72-fFPVmtlw-L-YpYTkVPysrlMOO8lFbOCptzmhsnoQBTqbLIrDFCMemEcZlSoCprbWGUpTOaM0mV4FN0Ne6mv-97F3u9bfehSS81Y4JxyXKARNGRsqGNMbhKd8HvTBg0BX0QqEeBOgnUB4H6sMzGTkxs8-bC3_L_pR985nSl</recordid><startdate>20130901</startdate><enddate>20130901</enddate><creator>Sheikhalishahi, M.</creator><creator>Ebrahimipour, V.</creator><creator>Shiri, H.</creator><creator>Zaman, H.</creator><creator>Jeihoonian, M.</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20130901</creationdate><title>A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem</title><author>Sheikhalishahi, M. ; 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| ispartof | International journal of advanced manufacturing technology, 2013-09, Vol.68 (1-4), p.317-338 |
| issn | 0268-3768 1433-3015 |
| language | eng |
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| subjects | CAE) and Design Computer-Aided Engineering (CAD Computing time Engineering Genetic algorithms Industrial and Production Engineering Mechanical Engineering Media Management Original Article Particle swarm optimization Redundancy Reliability engineering System reliability |
| title | A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem |
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