Opposition-Based Differential Evolution
Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the diffe...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 2008-02, Vol.12 (1), p.64-79 |
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| creator | Rahnamayan, S. Tizhoosh, H.R. Salama, M.M.A. |
| description | Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy. |
| doi_str_mv | 10.1109/TEVC.2007.894200 |
| format | Article |
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However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). 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However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.</description><subject>Acceleration</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Convergence</subject><subject>Design engineering</subject><subject>Differential evolution (DE)</subject><subject>Evolution</subject><subject>Evolutionary</subject><subject>Evolutionary algorithms</subject><subject>Exact sciences and technology</subject><subject>Fuzzy</subject><subject>Genetic mutations</subject><subject>Jumping</subject><subject>Laboratories</subject><subject>Machine intelligence</subject><subject>Mathematical models</subject><subject>opposite numbers</subject><subject>opposition-based learning</subject><subject>optimiztion</subject><subject>Pattern analysis</subject><subject>Robust control</subject><subject>System analysis and design</subject><subject>Systems engineering and theory</subject><issn>1089-778X</issn><issn>1941-0026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kM1LAzEQxYMoWKt3wUsRtKetk69NctRaP6DQSxVvIWYT2LLdXZOu4H9vli09ePAyM_B-M8x7CF1imGEM6m69eJ_PCICYScVSP0IjrBjOAEh-nGaQKhNCfpyisxg3AJhxrEZoumrbJpa7sqmzBxNdMXksvXfB1bvSVJPFd1N1vXiOTryporvY9zF6e1qs5y_ZcvX8Or9fZpYRvsusUk55RcFgLgv86X1OhMHSSJorIF6IQlrrJDGpUolt7pNICuIBW5kzOkbT4W4bmq_OxZ3eltG6qjK1a7qopeDJCSckkbf_kpRxIgntwes_4KbpQp1caJm-E1gAJAgGyIYmxuC8bkO5NeFHY9B9wLoPWPcB6yHgtHKzv2uiNZUPprZlPOwlhAmu8sRdDVzpnDvIjPLkRdFf9iSB3A</recordid><startdate>20080201</startdate><enddate>20080201</enddate><creator>Rahnamayan, S.</creator><creator>Tizhoosh, H.R.</creator><creator>Salama, M.M.A.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). 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| subjects | Acceleration Algorithms Applied sciences Artificial intelligence Computer science control theory systems Convergence Design engineering Differential evolution (DE) Evolution Evolutionary Evolutionary algorithms Exact sciences and technology Fuzzy Genetic mutations Jumping Laboratories Machine intelligence Mathematical models opposite numbers opposition-based learning optimiztion Pattern analysis Robust control System analysis and design Systems engineering and theory |
| title | Opposition-Based Differential Evolution |
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