Computing opposition by involving entire population

Rahnamayan, Shahryar; Jesuthasan, Jude; Bourennani, Farid; Salehinejad, Hojjat; Naterer, G.F.

doi:10.1109/cec.2014.6900329

Cited by 47 publications

(17 citation statements)

References 23 publications

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“…Then, an opposition operation is executed on

X_{i b}

. There are various opposition techniques, such as opposition‐based learning (OBL), 37 quasi OBL, 38 generalized OBL, 39 and centroid OBL 40 . In our approach, generalized OBL is utilized as follows 39

o x_{i b}^{d} = r n d_{3}^{d} \cdot (M a x^{d} + M i n^{d}) - x_{i b}^{d}

where

d = 1, 2, \dots, D, O X_{i b} = (o x_{i b}^{1}, o x_{i b}^{2}, \dots, o x_{i b}^{D})

is the opposite solution of

X_{i b}, r n d_{3}^{d} \in [0, 1]

is a random number, and

[M i n^{d}, M a x^{d}]

is the same with Equation (5).…”

Section: Proposed Approach (Mssnafa)mentioning

confidence: 99%

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An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Wang

Zhou

et al. 2021

Int J Intell Syst

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Firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. Though FA was employed to solve various optimization problems, it still has some deficiencies, such as high complexity, slow convergence rate, and low precision of solutions. To tackle these issues, this paper proposes an efficient FA based on modified search strategy and neighborhood attraction (namely MSSNaFA). In MSSNaFA, there are four main modifications. First, a novel search strategy based on dimension differences is designed. The attractiveness in the original FA is related to the Euclidean distance, while our new method uses the differences of each dimension for two fireflies to compute the attractiveness. Then, a modified neighborhood attraction mechanism is utilized to reduce the computational complexity. When the current solution is selected, it will move to the global best solution based on the new movement strategy. Third, for each firefly, three neighborhood search operations are carried out based on a preset probability. Lastly, the step factor is adaptively adjusted in the search process. Performance validation between MSSNaFA and four other FA variants show the effectiveness of our approach.

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“…Then, an opposition operation is executed on

X_{i b}

o x_{i b}^{d} = r n d_{3}^{d} \cdot (M a x^{d} + M i n^{d}) - x_{i b}^{d}

where

d = 1, 2, \dots, D, O X_{i b} = (o x_{i b}^{1}, o x_{i b}^{2}, \dots, o x_{i b}^{D})

is the opposite solution of

X_{i b}, r n d_{3}^{d} \in [0, 1]

is a random number, and

[M i n^{d}, M a x^{d}]

is the same with Equation (5).…”

Section: Proposed Approach (Mssnafa)mentioning

confidence: 99%

“…There are various opposition techniques, such as opposition-based learning (OBL), 37 quasi OBL, 38 generalized OBL, 39 and centroid OBL. 40 In our approach, generalized OBL is utilized as follows 39 where CX cx cx cx = ( , , …, )…”

Section: Neighborhood Searchmentioning

confidence: 99%

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Wang

Zhou

et al. 2021

Int J Intell Syst

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“…We also look at different ways of calculating opposition in general. Given any function y = f (x) with variables x ∈ [x min , x max ] with a sample average of x, one may calculate oppositeness for the input domain in different ways [32], [35], [25], [24] (% denotes the modulo operator):…”

Section: Learning Opposites Via Evolving Rulesmentioning

confidence: 99%

Learning opposites with evolving rules

Tizhoosh

Rahnamayan

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their "opposition-based" version to become faster and/or more accurate. However, most works still use a simple notion of opposites, namely linear (or type-I) opposition, that for each x ∈ [a, b] assigns its opposite as xI = a + b − x. This, of course, is a very naive estimate of the actual or true (non-linear) oppositexII , which has been called type-II opposite in literature. In absence of any knowledge about a function y = f (x) that we need to approximate, there seems to be no alternative to the naivety of type-I opposition if one intents to utilize oppositional concepts. But the question is if we can receive some level of accuracy increase and time savings by using the naive opposite estimatexI according to all reports in literature, what would we be able to gain, in terms of even higher accuracies and more reduction in computational complexity, if we would generate and employ true opposites? This work introduces an approach to approximate type-II opposites using evolving fuzzy rules when we first perform "opposition mining". We show with multiple examples that learning true opposites is possible when we mine the opposites from the training data to subsequently approximatexII = f (x, y).

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“…By vectorized randomizing the mutation scale factor (called MDEVM), which is a random mutation scale factor for each dimension of each individual, the micro-DE performs much better than utilizing a static mutation scale factor [3]. Methods such as opposition-based DE (ODE) [5] and centroid DE [6]- [8] have solely improved the performance of DE algorithm by proper diversifying the population in DE algorithm. These methods can also increase the performance of MDE.…”

Section: Introductionmentioning

confidence: 99%

Opposition-based ensemble micro-differential evolution

Salehinejad

Rahnamayan

Tizhoosh

2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE). A small population size decreases the computational complexity but also reduces the exploration ability of DE by limiting the population diversity. In this paper, we propose the idea of combining ensemble mutation scheme selection and opposition-based learning concepts to enhance the diversity of population in MDE at mutation and selection stages. The proposed algorithm enhances the diversity of population by generating a random mutation scale factor per individual and per dimension, randomly assigning a mutation scheme to each individual in each generation, and diversifying individuals selection using opposition-based learning. This approach is easy to implement and does not require the setting of mutation scheme selection and mutation scale factor. Experimental results are conducted for a variety of objective functions with low and high dimensionality on the CEC Black-Box Optimization Benchmarking 2015 (CEC-BBOB 2015). The results show superior performance of the proposed algorithm compared to the other micro-DE algorithms.

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Computing opposition by involving entire population

Cited by 47 publications

References 23 publications

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Learning opposites with evolving rules

Opposition-based ensemble micro-differential evolution

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