“…On the other the results of two other variants of DE, i.e. DE1 and DE2, are taken from [53] and [54], respectively. The results in terms of the statistical metrics are reported in Table 9 for 10 dimensional (10D) CEC 2015 problems.…”
Section: E Investigation On Cec 2015 Expensive Optimization Problemsmentioning
A novel optimization algorithm named hybrid grey wolf optimizer with crow search algorithm (GWO-CSA) is developed in this paper for handling large-scale numerical optimization problems. The proposed GWO-CSA algorithm combines the strong points of both grey wolf optimizer (GWO) and crow search algorithm (CSA) with the aim to escape from local optima with faster convergence than the standard GWO and CSA. In this algorithm, GWO operates in enhancing the exploration ability while CSA works as a local searching scheme to emphasize the exploitation capability to achieve global optimal solutions. In this sense, the movement direction and speed of leader grey wolf (alpha) is improved by incorporating the CSA phase. Also, a dynamic fuzzy learning strategy (DFLS) is introduced to enable the occurring of tiny changes in the neighborhood of the best solution to avoid the caught in the local optima and refine the quality of the obtained solution. The robustness and efficiency of the proposed GWO-CSA algorithm are investigated on fifteen CEC 2015 benchmark problems in addition to four large-scale problems and four real applications related to engineering design optimization taken from the literature. The comprehensive comparisons with other algorithms have demonstrated the effectiveness of GWO-CSA to address optimization tasks. INDEX TERMS grey wolf optimizer, crow search algorithm, numerical optimization, hybridization.
“…On the other the results of two other variants of DE, i.e. DE1 and DE2, are taken from [53] and [54], respectively. The results in terms of the statistical metrics are reported in Table 9 for 10 dimensional (10D) CEC 2015 problems.…”
Section: E Investigation On Cec 2015 Expensive Optimization Problemsmentioning
A novel optimization algorithm named hybrid grey wolf optimizer with crow search algorithm (GWO-CSA) is developed in this paper for handling large-scale numerical optimization problems. The proposed GWO-CSA algorithm combines the strong points of both grey wolf optimizer (GWO) and crow search algorithm (CSA) with the aim to escape from local optima with faster convergence than the standard GWO and CSA. In this algorithm, GWO operates in enhancing the exploration ability while CSA works as a local searching scheme to emphasize the exploitation capability to achieve global optimal solutions. In this sense, the movement direction and speed of leader grey wolf (alpha) is improved by incorporating the CSA phase. Also, a dynamic fuzzy learning strategy (DFLS) is introduced to enable the occurring of tiny changes in the neighborhood of the best solution to avoid the caught in the local optima and refine the quality of the obtained solution. The robustness and efficiency of the proposed GWO-CSA algorithm are investigated on fifteen CEC 2015 benchmark problems in addition to four large-scale problems and four real applications related to engineering design optimization taken from the literature. The comprehensive comparisons with other algorithms have demonstrated the effectiveness of GWO-CSA to address optimization tasks. INDEX TERMS grey wolf optimizer, crow search algorithm, numerical optimization, hybridization.
“…2019, 11, 1046 6 of 24 rain-like sound with their feet to attract earthworms hidden under the ground. Seagulls can drink both fresh and salt water [45], that can be seen in Figure 3. 3D-PCNN model, and the segmentation result graph is output.…”
Section: Seagull Optimization Algorithmmentioning
confidence: 99%
“…They use bread crumbs to attract fish and produce rain-like sound with their feet to attract earthworms hidden under the ground. Seagulls can drink both fresh and salt water [45], that can be seen in Figure 3. The mathematical models of migration and attacking the prey are discussed.…”
This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.
“…These metaheuristics maintain the diversity in population and avoid the solutions are being stuck in local optima. Some of well-known population-based metaheuristic algorithms are genetic algorithm (GA) [ 135 ], particle swarm optimization (PSO) [ 101 ], ant colony optimization (ACO) [ 47 ], spotted hyena optimizer (SHO) [ 41 ], emperor penguin optimizer (EPO) [ 42 ], and seagull optimization (SOA) [ 43 ]. Fig.…”
In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.
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