Using Hybrid Dependency Identification with a Memetic Algorithm for Large Scale Optimization Problems

Sayed, Eman; Essam, Daryl; Sarker, Ruhul A.

doi:10.1007/978-3-642-34859-4_17

Cited by 14 publications

(3 citation statements)

References 12 publications

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“…Enthused from this effort a CCEA with an adaptive segregating called CCEA-AVP (Singh and Ray, 2010) was announced. In Sayed et al (2012) and , it is exposed that approaches founded on correlation coefficient practice large number of computational assets but flop to recognise the nonlinear dependences amongst the variables. To recover from these issues, a novel technique named contribution-based cooperative coevolution algorithm (CBCC) was announced, where a computational expensive is allied with individual subcomponent conferring to their influences.…”

Section: Decomposition-based Algorithmsmentioning

confidence: 99%

A review of evolutionary algorithms in solving large scale benchmark optimisation problems

Mohapatra¹,

Roy²,

Das³

et al. 2022

IJMOR

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Optimisation problems containing huge total of decision variables are termed as large scale global optimisation problems which are often considered as abundant challenges to the area of optimisation. With presence of large number of decision variables, these problems also used to have the property of nonlinearity, discontinuity and multi-modality. Hence, the nature-inspired optimisation algorithms based on stochastic approaches are termed as great saviours than the deterministic approaches to handle these problems. However, the nature inspired optimisation algorithms also suffer from the jinx of dimensionality in the decision variable space. With increase of dimensions in the decision variable space, the complexity of the problem also increases exponentially. Hence, there is an immense need of proper guidance of choosing capable nature inspired algorithms to solve real-life large scale optimisation problems. In this paper, an attempt has been made to select the elite algorithm with proper justification. Hence, a number of works have been presented to analyse the results and to tackle the difficulty.

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Section: Decomposition-based Algorithmsmentioning

confidence: 99%

A review of evolutionary algorithms in solving large scale benchmark optimisation problems

Mohapatra¹,

Roy²,

Das³

et al. 2022

IJMOR

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“…DIMA is revised and a new updated version of DIMA, HDIMA [21], is proposed. The three main stages of HDIMA are as follows:…”

Section: Memetic Algorithmsmentioning

confidence: 99%

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

Singh¹,

Jana²

2017

IJCA

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In recent years, there has been a remarkable improvement in the computing power of computers. As a result, numerous realworld optimization problems in science and engineering, possessing very high dimensions, have appeared. In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems. Broadly these algorithms can be categorized in 3 groups: Standard Evolutionary Algorithms, Cooperative Co-evolution (CC) based Evolutionary Algorithms and Memetic Algorithms. This paper gives a brief introduction of some state-of-the-art Metaheuristics used in the field of LSGO, discusses their performance in CEC Competition on LSGO and finally, future scope in this field is presented.

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“…The way of projecting joint space is known to be related to the performance of CCEAs [13], [6], [7], [14], [15], [16]. CCEAs suffer from relative over-generalization [17], if the decomposition separates interacting variables into different subproblems, the projection throws considerable amount of information away.…”

Section: A Cooperative Coevolutionmentioning

confidence: 99%

Impact of problem decomposition on Cooperative Coevolution

Chen

Tang

2013

2013 IEEE Congress on Evolutionary Computation

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Variable Interaction Learning (VIL) is an emerging technique regarding detecting interacting variables so that Cooperative Coevolutionary Evolutionary Algorithms (CCEAs) can decompose problems accordingly and tackle subproblems of smaller sizes. While previous approaches are developed to efficiently perform VIL, no study has been on the actual usefulness of the detected variable interactions in terms of the performance of CCEAs. Since VIL is a computationally expensive task by itself, overly spending time on VIL without notable benefits for CCEAs should be avoided. It is hence critical to study the real impact of problem decomposition on CCEAs. We conduct empirical studies to address three closely related questions: 1) will a better problem decomposition lead to better performance of CCEAs, 2) when will improving problem decomposition benefit CCEAs, and 3) to what extent will improving problem decomposition enhance the performance of CCEAs.

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Using Hybrid Dependency Identification with a Memetic Algorithm for Large Scale Optimization Problems

Cited by 14 publications

References 12 publications

A review of evolutionary algorithms in solving large scale benchmark optimisation problems

A review of evolutionary algorithms in solving large scale benchmark optimisation problems

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

Impact of problem decomposition on Cooperative Coevolution

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