A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems

Zhang, Zichen; Ding, Shifei; Jia, Weikuan

doi:10.1016/j.engappai.2019.06.017

Cited by 140 publications

(52 citation statements)

References 40 publications

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“…Finally, comparing the total electricity demand of the industry in responsive and non-responsive (traditional) states, it was shown that the plant can provide power flexibility to the upstream network; In industrial studies, the superiority of the suggested approaches should be investigated in comparison with the literature [34]. In this way, the key results are pointed out to show how the proposed approach outweighs the previous studies [35,36]. Therefore, the final results can be stated as follows:…”

Section: Discussionmentioning

confidence: 99%

“…From the demand side, Equation (34) describes the total demand of the ORI as the summation of steam demand in PRP and SRP. Total thermal energy needed to generate steam in boiler and CHP is illustrated by Equations (35) and (36), respectively.…”

Section: Energy Cost Optimizationmentioning

confidence: 99%

“…demand in PRP and SRP. Total thermal energy needed to generate steam in boiler and CHP is illustrated by Equations (35) and (36), respectively. Finally, Equation (37) describes the upper and lower thresholds of transactive power.…”

Section: Time Frameworkmentioning

confidence: 99%

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Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Golmohamadi

Asadi

2020

Energies

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This paper proposes a novel approach to optimize the main energy consumptions of heavy oil refining industries (ORI) in response to electricity price uncertainties. The whole industrial sub-processes of the ORI are modeled mathematically to investigate the joint power-heat flexibility potentials of the industry. To model the refinery processes, an input/output flow-based model is proposed for five main refining units. Moreover, the role of storage tanks capacity in the power system flexibility is investigated. To hedge against the electricity price uncertainty, an uncertain bound for the wholesale electricity price is addressed. To optimize the industrial processes, a dual robust mixed-integer quadratic program (R-MIQP) is adopted; therefore, the ORI’s operational strategies are determined under the worst-case realization of the electricity price uncertainty. Finally, the suggested approach is implemented in the south-west sector of the Iran Energy Market that suffers from a lack of electricity in hot days of summer. The simulation results confirm that the proposed framework ensures industrial demand flexibility to the external grids when a power shortage occurs. The approach not only provides demand flexibility to the power system, but also minimizes the operation cost of the industries.

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Section: Discussionmentioning

confidence: 99%

Section: Energy Cost Optimizationmentioning

confidence: 99%

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Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Golmohamadi

Asadi

2020

Energies

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“…Wen et al [83] proposed a new hybrid algorithm based on grey wolf optimizer and cuckoo search (GWOCS), which was developed to extract the parameters of different PV cell models with the experimental data under different operating conditions. Zhang et al [84] proposed an ensemble CS variant that divides the population into two subgroups and adopts CS and DE for these two subgroups independently. These two subgroups can exchange useful information by division.…”

Section: Related Workmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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“…In [ 33 ], GWO is hybridized with CS, i.e., GWOCS for the extraction of parameters for different PV cell models situated in different conditions. Zhang et al [ 34 ] developed an ensemble CS algorithm that foremost divides a population into two smaller groups and then utilizes CS and differential evolution (DE) on the derived subgroups independently. The subgroups are free to share useful information by division.…”

Section: Literature Reviewsmentioning

confidence: 99%

An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection

Segera

Mbuthia

Nyete

2020

BioMed Research International

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Finding an optimal set of discriminative features is still a crucial but challenging task in biomedical science. The complexity of the task is intensified when any of the two scenarios arise: a highly dimensioned dataset and a small sample-sized dataset. The first scenario poses a big challenge to existing machine learning approaches since the search space for identifying the most relevant feature subset is so diverse to be explored quickly while utilizing minimal computational resources. On the other hand, the second aspect poses a challenge of too few samples to learn from. Though many hybrid metaheuristic approaches (i.e., combining multiple search algorithms) have been proposed in the literature to address these challenges with very attractive performance compared to their counterpart standard standalone metaheuristics, more superior hybrid approaches can be achieved if the individual metaheuristics within the proposed hybrid algorithms are improved prior to the hybridization. Motivated by this, we propose a new hybrid Excited- (E-) Adaptive Cuckoo Search- (ACS-) Intensification Dedicated Grey Wolf Optimization (IDGWO), i.e., EACSIDGWO. EACSIDGWO is an algorithm where the step size of ACS and the nonlinear control strategy of parameter a→ of the IDGWO are innovatively made adaptive via the concept of the complete voltage and current responses of a direct current (DC) excited resistor-capacitor (RC) circuit. Since the population has a higher diversity at early stages of the proposed EACSIDGWO algorithm, both the ACS and IDGWO are jointly involved in local exploitation. On the other hand, to enhance mature convergence at latter stages of the proposed algorithm, the role of ACS is switched to global exploration while the IDGWO is still left conducting the local exploitation. To prove that the proposed algorithm is superior in providing a good learning from fewer instances and an optimal feature selection from information-rich biomedical data, all these while maintaining a high classification accuracy of the data, the EACSIDGWO is employed to solve the feature selection problem. The EACSIDGWO as a feature selector is tested on six standard biomedical datasets from the University of California at Irvine (UCI) repository. The experimental results are compared with the state-of-the-art feature selection techniques, including binary ant-colony optimization (BACO), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), and extended binary cuckoo search algorithm (EBCSA). These results reveal that the EACSIDGWO has comprehensive superiority in tackling the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. Furthermore, the superiority of the proposed algorithm is proved via various numerical techniques like ranking methods and statistical analysis.

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A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems

Cited by 140 publications

References 40 publications

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection

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