An opposition-based hybrid Artificial Bee Colony with differential evolution

Worasucheep, Chukiat

doi:10.1109/cec.2015.7257210

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…We can observe only minor difference between both figures. For OABCDE [24], each generation is composed of the following 3 actions: the employed bee's exploring, the onlooker bee's dancing and the opposition- Considering the time complexities of DMGSA and OABCDE, we can see why DMGSA (and GSA) took a longer running time than OABCDE in our GRN optimization in previous subsection.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, the proposed DMGSA, original GSA and OABCDE are employed to optimize a smallscale five-gene GRN problem in order to compare the performance. OABCDE is a hybrid algorithm of artificial bee colony optimization algorithm and DE with an opposition-based learning to enhance exploration and exploitation simultaneously [24]. Details and parameter settings of OABCDE can be reached from [24].…”

Section: Algorithms and Parameter Settingmentioning

confidence: 99%

“…OABCDE is a hybrid algorithm of artificial bee colony optimization algorithm and DE with an opposition-based learning to enhance exploration and exploitation simultaneously [24]. Details and parameter settings of OABCDE can be reached from [24]. For the case of DMGSA, we choose the optimal combination of G 0 = 300, α = 7 and β = 3, while CR are tested for both 0.15 and 0.85; thus they are named for brevity as DMGSA-15 and DMGSA-85, respectively.…”

Section: Algorithms and Parameter Settingmentioning

confidence: 99%

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Enhancement of Gravitational Search Algorithm using A Differential Mutation Operator and Its Application on Reconstructing Gene Regulatory Network

Worasucheep

2019

ECTI-CIT

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Gravitational Search Algorithm (GSA) is a recent stochastic search algorithm that is inspired from the concepts of gravity rule and law of motion in physics. Despite its success and attractiveness, it has some coefficients and parameters that should be properly tuned to improve its performance. This paper studies the performance of GSA by varying the parameters that controls its gravitational force. Then a new differential mutation operator is proposed to enhance performance of GSA by accelerating its convergence. The proposed algorithm, namely DMGSA, is evaluated using 15 well-known benchmark functions from the special session of CEC2013 with different characteristics including randomly shifted optimum, rotation and non-separability. The results obviously confirms the performance achieved from the proposed mutation operator outperforms that from the attempts of parameter tuning in the original GSA. Lastly, DMGSA is applied for optimizing a small-scale gene regulatory network. The result demonstrates that its performance is highly competitive and clearly surpasses original GSA.

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Section: Resultsmentioning

confidence: 99%

Section: Algorithms and Parameter Settingmentioning

confidence: 99%

Section: Algorithms and Parameter Settingmentioning

confidence: 99%

See 1 more Smart Citation

Enhancement of Gravitational Search Algorithm using A Differential Mutation Operator and Its Application on Reconstructing Gene Regulatory Network

Worasucheep

2019

ECTI-CIT

Self Cite

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“…They introduced four common kinds of opposition-based models into their new approach, where the new generated solution and opposition solution are used to enlarge the searching area and the new location of the scout is decided by the location of the employed bee to enhance local exploitation of the scout. Worasucheep (2015) introduced an opposition-based hybrid of ABC and DE algorithms for solving continuous problems. The proposed algorithm, called OABCDE, adopts mutation operation of DE and a crossover-like mechanism to improve the convergence ability of ABC without introducing new parameters.…”

Section: Using Opposition-based Learning In Abcmentioning

confidence: 99%

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

2020

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“…Lynn and Suganthan [28] used a self-adaptive scheme to identify the top algorithm from five PSO variants in each generation. Worasucheep [29] employed an efficient mutation operation of DE to enhance the convergence of ABC. Nguyen et al [30] proposed a hybrid method between Bat algorithm and ABC with a communication strategy.…”

Section: Introductionmentioning

confidence: 99%

Hunting Optimization: An New Framework for Single Objective Optimization Problems

Zhao

Wang

et al. 2019

IEEE Access

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Swarm intelligence algorithms play vital roles in objective optimization problems. To solve diverse and increasingly complicated problems, a new algorithm is always desired. This paper proposes a new optimization algorithm named hunting optimization based on human hunting activities. The population has consisted of huntsmen and hunting dogs. Each of them represents a feasible solution. In the evolution process, each huntsman shrinks its hunting ground with an adaptive reduction factor to concentrate on searching the most promising area. Then, each huntsman uses its dogs to search its local hunting ground and updates its position to a more promising place by its own searching results as well as the results of other huntsmen. At the same time, to further balance the exploration and exploitation, huntsmen with the least prey will be eliminated and their dogs will be distributed to others. Congestion detection is also applied to avoid getting stuck at a local optimum. The experimental results on 12 benchmark functions and CEC2013 test suites compared with 12 state-of-the-art algorithms demonstrate the effectiveness of the proposed method.

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An opposition-based hybrid Artificial Bee Colony with differential evolution

Cited by 4 publications

References 24 publications

Enhancement of Gravitational Search Algorithm using A Differential Mutation Operator and Its Application on Reconstructing Gene Regulatory Network

Enhancement of Gravitational Search Algorithm using A Differential Mutation Operator and Its Application on Reconstructing Gene Regulatory Network

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

Hunting Optimization: An New Framework for Single Objective Optimization Problems

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