Opposition-Based Learning: A New Scheme for Machine Intelligence

Tizhoosh, H.R.

doi:10.1109/cimca.2005.1631345

Cited by 1,432 publications

(821 citation statements)

References 3 publications

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“…The basic concept of OBL was originally introduced by Tizhoosh in [54]. The basic idea of OBL is to calculate the fitness not only of the current individual but also to calculate the fitness of the opposite individual.…”

Section: Opposition Based Learning (Obl)mentioning

confidence: 99%

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

Goudos

2016

Open Mathematics

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A major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization (GOBBO) algorithm which is enhanced with Oppositional Based Learning (OBL) techniques. We apply both the original BBO and the new Generalized Oppositional BBO (GOBBO) to the PTS problem. The GOBBO-PTS method is compared with other PTS schemes for PAPR reduction found in the literature. The simulation results show that GOBBO and BBO are in general highly efficient in producing significant PAPR reduction and reducing the PTS search complexity.

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Section: Opposition Based Learning (Obl)mentioning

confidence: 99%

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

Goudos

2016

Open Mathematics

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“…Opposition-based learning was proposed by Tizhoosh (Tizhoosh 2005) and it has been applied and tested in some heuristic optimization algorithms such as genetic algorithm (Tizhoosh 2005), differential evolution algorithm (Rahnamayan et al 2006), ant colony optimization (Malisia & Tizhoosh 2007) and gravitational search algorithm (Shaw et al 2012) in order to enhance the performance of these algorithms.…”

Section: Opposition-based Learningmentioning

confidence: 99%

“…The improvement made to FA in this research differs from previous works. In order to further improve the performance of original FA in terms of convergence rate, the opposition-based learning (Tizhoosh 2005) is integrated into FA while the idea of inertia weight FA (Yafei et al 2012) is also incorporated at the same time to improve the ability of FA to escape from local optimum.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Opposition-based Firefl y Algorithm for Solving Complex Optimization Problems

Wong¹,

Shareef²,

Mohamed³

et al. 2014

JKUKM

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“…OPPOSITION-BASED LEARNING Opposition-based learning (OBL) is proposed in [7]. OBL has first been utilized to improve learning and back propagation in neural networks [8], and since then it has been applied to many evolutionary algorithms such as differential evolution [5], particle swarm optimization [9] and ant colony optimization [10].…”

Section: Worst Solutionmentioning

confidence: 99%

Biogeography‐Based Optimization

Bozorg‐Haddad

Solgi

Loáiciga

2017

Meta‐Heuristic and Evolutionary Algorithms for Engineering Optimization

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Abstract-We propose a novel variation to biogeographybased optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs opposition-based learning (OBL) alongside BBO's migration rates to create oppositional BBO (OB BO). Additionally, a new opposition method named quasi-reflection is introduced. Quasi-reflection is based on opposite numbers theory and we mathematically prove that it has the highest expected probability of being closer to the problem solution among all OBL methods. The oppositional algorithm is further revised by the addition of dynamic domain scaling and weighted reflection. Simulations have been performed to validate the performance of quasiopposition as well as a mathematical analysis for a singledimensional problem. Empirical results demonstrate that with the assistance of quasi-reflection, OB BO significantly outperforms BBO in terms of success rate and the number of fitness function evaluations required to find an optimal solution.Index Terms-Biogeography-based optimization (BBO), evolutionary algorithms, opposition-based learning, opposite numbers, quasi-opposite numbers, quasi-reflected numbers, probability.

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Opposition-Based Learning: A New Scheme for Machine Intelligence

Cited by 1,432 publications

References 3 publications

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

An Enhanced Opposition-based Firefl y Algorithm for Solving Complex Optimization Problems

Biogeography‐Based Optimization

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