ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm

Venske, Sandra M.; Gonçalves, Richard A.; Delgado, Myriam Regattieri

doi:10.1016/j.neucom.2013.06.043

Cited by 70 publications

(20 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method calculates the normalized relative fitness improvements from successful operators, and then it regards the mean value of the improvements brought by operators as the credit. In [26], four different credit assignment methods are adopted, which are Average Absolute Reward, Extreme Absolut Reward, Average Normalized Reward, and Extreme Normalized Reward. All the rewards of each method are evaluated, and the method with the max probability is chosen as the credit assignment method at current generation.…”

Section: Credit Assignmentmentioning

confidence: 99%

“…If the new one is better than the old one, the former replaces the latter. The reward Δ is the same as the relative improvement in [26], and it is given by…”

Section: Moea/d-dra With Lsiasmentioning

confidence: 99%

“…Inspired by SlMAB and rank based credit assignment, Li et al [16] suggest that a decay factor is useful for the credit assignment and it can improve the selection probability of the best operator. Two modified MAB methods are provided, called UCB-Tuned and UCB-V, in which reward variance is used as a parameter for a better EvE tradeoff [26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Gao

Qing-hua

Fan

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems.

show abstract

Section: Credit Assignmentmentioning

confidence: 99%

“…If the new one is better than the old one, the former replaces the latter. The reward Δ is the same as the relative improvement in [26], and it is given by…”

Section: Moea/d-dra With Lsiasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Gao

Qing-hua

Fan

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…), [95,96,97,98]. The experiments on each algorithm were repeated 20 times independently to find Tables 2-5 show the comparison of (best) results over applied benchmarking problems.…”

Section: Benchmarking the Proposed Intelligent Classifier (Predictor)mentioning

confidence: 99%

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Karami

Zapata

2015

Neurocomputing

View full text Add to dashboard Cite

Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria.

show abstract

“…Recently, the adaptive DE algorithm has been used for optimization within different domains [38,17,2,33,32,3,18], and many others, which clearly indicates the high usability of adaptive and selfadaptive mechanism in the DE algorithms.…”

Section: Literature Overviewmentioning

confidence: 99%

Adaptation in the Differential Evolution

Brest

Zamuda

Bošković

2015

Adaptation, Learning, and Optimization

View full text Add to dashboard Cite

Abstract. This chapter gives an overview of Differential Evolution (DE), then presents adaptive and self-adaptive mechanisms within the DE algorithm. They can be used in order to make a DE solver more robust, efficient, etc., and to overcome parameter tuning which is usually a timeconsuming task needed to be done before the actual optimization process starts. Literature overviews of adaptive and self-adaptive mechanisms are mainly focused on mutation and crossover DE operations, but less on population size adaptation. Some experiments have been performed on benchmark functions to present both the advantages and disadvantages of using self-adaptive mechanisms.

show abstract

ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm

Cited by 70 publications

References 33 publications

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Adaptation in the Differential Evolution

Contact Info

Product

Resources

About