Sports inspired computational intelligence algorithms for global optimization

Alataş, Bilal

doi:10.1007/s10462-017-9587-x

Cited by 43 publications

(16 citation statements)

References 83 publications

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“…However, the increased data dimension may lead to an increase in time complexity, and feature selection is always utilized to solve the problem. As the essence of feature selection is a combinatorial optimization problem, which means selecting a satisfactory feature subset to conduct building extraction, it is usually solved by swarm intelligence algorithms [27]. In this paper, by fusing the point cloud and texture features, as well as conducting feature selection, a building extraction technique is realized.…”

Section: Introductionmentioning

confidence: 99%

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

Lai

Yang

et al. 2019

Remote Sensing

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Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

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Section: Introductionmentioning

confidence: 99%

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

Lai

Yang

et al. 2019

Remote Sensing

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“…One important feature of the MVPA is that it has no internal parameter to tune. In [41], a comparative study was carried out between metaheuristic algorithms inspired by sports events including the MVPA. The MVPA has been ranked first for unimodal problems and equally ranked first with two other algorithms for multimodal problems.…”

Section: Most Valuable Player Algorithmmentioning

confidence: 99%

Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources

2019

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Substituting a single large power grid into various manageable microgrids is the emerging form for maintaining power systems. A microgrid is usually comprised of small units of renewable energy sources, battery storage, combined heat and power (CHP) plants and most importantly, an energy management system (EMS). An EMS is responsible for the core functioning of a microgrid, which includes establishing continuous and reliable communication among all distributed generation (DG) units and ensuring well-coordinated activities. This research focuses on improving the performance of EMS. The problem at hand is the optimal scheduling of the generation units and battery storage in a microgrid. Therefore, EMS should ensure that the power is shared among different sources following an imposed scenario to meet the load requirements, while the operational costs of the microgrid are kept as low as possible. This problem is formulated as an optimization problem. To solve this problem, this research proposes an enhanced version of the most valuable player algorithm (MVPA) which is a new metaheuristic optimization algorithm, inspired by actual sporting events. The obtained results are compared with numerous well-known optimization algorithms to validate the efficiency of the proposed EMS.Sustainability 2019, 11, 3839 2 of 28 algorithms were used to optimize power sharing among active DGs. In order to determine the optimal component sizing in a microgrid, the mixed integer linear programming (MILP) method has been used to model the microgrid components with consideration of demand response [5]. In another study [6], similar sizing problems of microgrid components were solved using a genetic algorithm (GA) and the energy management issue was formulated using MILP. In addition to component sizing, system configuration was optimized in [7] using the multi-objective PSO by taking into account production cost, reliability, and environmental impact. The studied system comprised of a diesel generator, solar panels, wind turbines, and battery storage. Several methods have also been used for optimizing multi variable problems of energy management systems (EMS) in microgrids [8][9][10][11][12][13]. Wang et al. [14] investigated EMS of a microgrid with multi period optimization problems using an MPI based PSO algorithm and concluded that the proposed algorithm could be effectively used to improve the operation time.Four techniques were used in [15] to determine the optimized operational strategy for an entire day based on least operating cost and minimum carbon emissions. These techniques were a non-dominated sorting genetic algorithm (NSGA), multi-objective PSO, multi-objective uniform water cycle algorithm (WCA), and normal constraint algorithm. Reference [16] presented a real time EMS that had the tendency to optimally minimize carbon emissions and energy cost, and simultaneously maximized the power coming from renewable DGs using binary PSO. An improved binary PSO with double-structure coding was applied to optimize a microgrid operatio...

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“…In [29], a comparison has been conducted between the MVPA and other sport-inspired metaheuristic algorithms. All compared algorithms have been tested using unimodal and multimodal problems.…”

Section: Vewls (Vertex Edge Weighting Local Search) Algorithmmentioning

confidence: 99%

Most Valuable Player Algorithm for Solving Minimum Vertex Cover Problem

Khattab¹,

Sharieh²,

Mahafzah³

2019

IJACSA

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Minimum Vertex Cover Problem (MVCP) is a combinatorial optimization problem that is utilized to formulate multiple real-life applications. Owing to this fact, abundant research has been undertaken to discover valuable MVCP solutions. Most Valuable Player Algorithm (MVPA) is a recently developed metaheuristic algorithm that inspires its idea from team-based sports. In this paper, the MVPA_MVCP algorithm is introduced as an adaptation of the MVPA for the MVCP. The MVPA_MVCP algorithm is implemented using Java programming language and tested on a Microsoft Azure virtual machine. The performance of the MVPA_MVCP algorithm is evaluated analytically in terms of run time complexity. Its average-case run time complexity is ceased to , where I is the size of the initial population, |V| is the number of vertices and |E| is the number of edges of the tested graph. The MVPA_MVCP algorithm is evaluated experimentally in terms of the quality of gained solutions and the run time. The experimental results over 15 instances of DIMACS benchmark revealed that the MVPA_MVCP algorithm could, in the best case, get the best known optimal solution for seven data instances. Also, the experimental findings exposed that there is a direct relation between the number of edges of the graph under test and the run time.

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Sports inspired computational intelligence algorithms for global optimization

Cited by 43 publications

References 83 publications

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources

Most Valuable Player Algorithm for Solving Minimum Vertex Cover Problem

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