Hybrid self-adaptive cuckoo search for global optimization

Mlakar, Uroš; Fister, Iztok

doi:10.1016/j.swevo.2016.03.001

Cited by 102 publications

(40 citation statements)

References 50 publications

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“…e relationship between individuals can range from mother-child bond, bond groups, independent males, and strangers. e paper proposes a novel swarm intelligence-based metaheuristic optimization algorithm [37], jDE [39], CETMS [40], FWA-DM [41], TSC-PSO [42], and HCA-SA [43] algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In order to test the efficiency and reliability of the algorithm, it has been tested on CEC 2014 [28] test set against some of the newly proposed algorithms. ese include L-SHADE [36], MVMO [37,38], jDE [39], CETMS [40], FWA-DM [41], TSC-PSO [42], and HCA-SA [43] algorithms. L-Shade [36] proposed by Tanabe et al is a modification of the SHADE [44] algorithm incorporating linear population size reduction (LPSR) strategy within it.…”

Section: Evaluation On Cec 2014 Benchmark Functionsmentioning

confidence: 99%

“…TSC-PSO [42] applies spatial correlation-based movement strategy on the PSO [17] algorithm. HCA-SA [43] introduces self-adaptivity in the vanilla cuckoo search algorithm (CSA) [46]. Tables 15 and 16 compares the proposed algorithm with L-SHADE [36], MVMO [37,38], jDE [39], CETMS [40], FWA-DM [41], TSC-PSO [42], and HCA-SA [43] algorithms on CEC 2014 [28] test set.…”

Section: Evaluation On Cec 2014 Benchmark Functionsmentioning

confidence: 99%

See 2 more Smart Citations

Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems

Sharma

Kumar

Saxena

et al. 2018

Modelling and Simulation in Engineering

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In this paper, a novel swarm intelligence-based ensemble metaheuristic optimization algorithm, called Structured Clanning-based Ensemble Optimization, is proposed for solving complex numerical optimization problems. e proposed algorithm is inspired by the complex and diversified behaviour present within the fission-fusion-based social structure of the elephant society. e population of elephants can consist of various groups with relationship between individuals ranging from mother-child bond, bond groups, independent males, and strangers. e algorithm tries to model this individualistic behaviour to formulate an ensemble-based optimization algorithm. To test the efficiency and utility of the proposed algorithm, various benchmark functions of different geometric properties are used. e algorithm performance on these test benchmarks is compared to various state-ofthe-art optimization algorithms. Experiments clearly showcase the success of the proposed algorithm in optimizing the benchmark functions to better values.

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation On Cec 2014 Benchmark Functionsmentioning

confidence: 99%

Section: Evaluation On Cec 2014 Benchmark Functionsmentioning

confidence: 99%

See 1 more Smart Citation

Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems

Sharma

Kumar

Saxena

et al. 2018

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

show abstract

“…Later studies had different interpretations of Lévy flight such as [33,34]. In this paper, the strategy used for Lévy flight is given as:…”

Section: Differential Strategy For Lévy Flightmentioning

confidence: 99%

A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem

Yang

Zhou

et al. 2018

Algorithms

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Abstract:In this paper, a gradient-based cuckoo search algorithm (GCS) is proposed to solve a reservoir-scheduling problem. The classical cuckoo search (CS) is first improved by a self-adaptive solution-generation technique, together with a differential strategy for Lévy flight. This improved CS is then employed to solve the reservoir-scheduling problem, and a two-way solution-correction strategy is introduced to handle variants' constraints. Moreover, a gradient-based search strategy is developed to improve the search speed and accuracy. Finally, the proposed GCS is used to obtain optimal schemes for cascade reservoirs in the Jinsha River, China. Results show that the mean and standard deviation of power generation obtained by GCS are much better than other methods. The converging speed of GCS is also faster. In the optimal results, the fluctuation of the water level obtained by GCS is small, indicating the proposed GCS's effectiveness in dealing with reservoir-scheduling problems.

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“…These techniques, also referred to as global search optimization techniques, are based on evolutionary computation that mimics animal behavior and human evolution. Other than being able to locate global minima, non-gradient algorithms do not rely on a single variable initialization and are capable of adapting to random noise [19][20][21].…”

mentioning

confidence: 99%

Noise cancellation in cognitive radio systems: A performance comparison of evolutionary algorithms

Quadri

Manesh

Kaabouch

2017

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)

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Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.

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Hybrid self-adaptive cuckoo search for global optimization

Cited by 102 publications

References 50 publications

Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems

Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems

A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem

Noise cancellation in cognitive radio systems: A performance comparison of evolutionary algorithms

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