Multiobjective evolutionary algorithms: A survey of the state of the art

Zhou, Aimin; Qu, Boyang; Li, Hui; Zhao, Shi-Zheng; Suganthan, Ponnuthurai Nagaratnam; Zhang, Qingfu

doi:10.1016/j.swevo.2011.03.001

Cited by 1,801 publications

(674 citation statements)

References 242 publications

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“…3) Archiving to prevent degradation in fitness during successive generations by maintaining an external population to preserve the best solutions and for periodic input to the main population. A detailed survey of multiobjective algorithms is available in [287,288]. Now, based on the above discussion on the cost functions and generalization conditions, the multiobjective for FNN optimization can be categorized as non-Pareto based multiobjective approach and Pareto-based multiobjective approach.…”

Section: Multiobjective Metaheuristic Approachesmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

408

145

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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Section: Multiobjective Metaheuristic Approachesmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

408

145

View full text Add to dashboard Cite

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“…DE has both the features; it is speedy as well as diversity. DE algorithm has a balance between diversity and exploration (Devi et al, 2014;Zhou et al, 2011). This paper is organized as follows: in section 3 Network model is discussed.…”

Section: Literature Surveymentioning

confidence: 99%

Channel assignment using differential evolution algorithm in cognitive radio networks

Latif¹

2017

Int. j. adv. appl. sci.

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The emerging wireless applications have increased the demand of wireless spectrum significantly. Present spectrum assignment is static, due to which problem of spectrum scarcity has been raised. Cognitive Radio (CR) is a promising technology to deal with spectrum scarcity problem, which uses dynamic spectrum allocation to utilize the vacant spectrum. The CR intelligently scans the spectrum in its vicinity and search the vacant spectrum. The optimization of available spectrum is important research challenge in cognitive radio networks (CRNs). In this research work, we have optimized the spectrum utility of SUs using Differential Evolution (DE) algorithm in order to reduce the interference incurs to primary users (PUs) and as well as among the secondary users (SUs). Moreover, the results are compared with other evolutionary channel assignment algorithms like Fuzzy Logic Ant Colony System (FLACS) and Color Sensitive Graph Coding Method (CSGC). It has been observed that the results of proposed algorithms can further enhance the spectrum utility in CRNs in comparison to FLACS and CSGC.

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“…As depicted by [38], genetic based approaches are suitable candidate for scheduling and planning problems. Genetic Algorithms (GA) are driven by elitism rules that favor the survival of strongest species (best candidate solutions) in analogy to natural selection [36].…”

Section: B Moeas Conceptsmentioning

confidence: 99%

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

Kateb

Fouquet

Bourcier

et al. 2014

2014 14th International Conference on Quality Software

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Abstract-Elasticity is a key feature for cloud infrastructures to continuously align allocated computational resources to evolving hosted software needs. This is often achieved by relaxing quality criteria, for instance security or privacy because quality criteria are often conflicting with performance. As an example, software replication could improve scalability and uptime while decreasing privacy by creating more potential leakage points. The conciliation of these conflicting objectives has to be achieved by exhibiting trade-offs. Multi-Objective Evolutionary Algorithms (MOEAs) have shown to be suitable candidates to find these trade-offs and have been even applied for cloud architecture optimizations. Still though, their runtime efficiency limits the widespread adoption of such algorithms in cloud engines, and thus the consideration of quality criteria in clouds. Indeed MOEAs produce many dead-born solutions because of the Darwinian inspired natural selection, which results in a resources wastage. To tackle MOEAs efficiency issues, we apply a process similar to modern biology. We choose specific artificial mutations by anticipating the optimization effect on the solutions instead of relying on the randomness of natural selection. This paper introduces the Sputnik algorithm, which leverages the past history of actions to enhance optimization processes such as cloud elasticity engines. We integrate Sputnik in a cloud elasticity engine, dealing with performance and quality criteria, and demonstrate significant performance improvement, meeting the runtime requirements of cloud optimization.

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Multiobjective evolutionary algorithms: A survey of the state of the art

Cited by 1,801 publications

References 242 publications

Metaheuristic design of feedforward neural networks: A review of two decades of research

Metaheuristic design of feedforward neural networks: A review of two decades of research

Channel assignment using differential evolution algorithm in cognitive radio networks

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

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