This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes...This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes of spatial entities or objects. We introduce the theory of spatial knowledge expression system and some concepts including comprehensive knowledge discovery and spatial union information table (SUIT). In theory, SUIT records all information contained in the studied objects, but in reality, because of the complexity and varieties of spatial relations, only those factors of interest to us are selected. In order to find out the comprehensive knowledge from spatial databases, an efficient comprehensive knowledge discovery algorithm called recycled algorithm (RAR) is suggested.展开更多
This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis b...This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis based on human behavior in which people gain and share their knowledgewith others. Different types of stochastic fractional programming problemsare considered in this study. The augmented Lagrangian method (ALM)is used to handle these constrained optimization problems by convertingthem into unconstrained optimization problems. Three examples from theliterature are considered and transformed into their deterministic form usingthe chance-constrained technique. The transformed problems are solved usingGSK algorithm and the results are compared with eight other state-of-the-artmetaheuristic algorithms. The obtained results are also compared with theoptimal global solution and the results quoted in the literature. To investigatethe performance of the GSK algorithm on a real-world problem, a solidstochastic fixed charge transportation problem is examined, in which theparameters of the problem are considered as random variables. The obtainedresults show that the GSK algorithm outperforms other algorithms in termsof convergence, robustness, computational time, and quality of obtainedsolutions.展开更多
基金theChina’sNationalSurveyingTechnicalFund (No .2 0 0 0 7)
文摘This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes of spatial entities or objects. We introduce the theory of spatial knowledge expression system and some concepts including comprehensive knowledge discovery and spatial union information table (SUIT). In theory, SUIT records all information contained in the studied objects, but in reality, because of the complexity and varieties of spatial relations, only those factors of interest to us are selected. In order to find out the comprehensive knowledge from spatial databases, an efficient comprehensive knowledge discovery algorithm called recycled algorithm (RAR) is suggested.
基金The research is funded by Researchers Supporting Program at King Saud University,(Project#RSP-2021/305).
文摘This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis based on human behavior in which people gain and share their knowledgewith others. Different types of stochastic fractional programming problemsare considered in this study. The augmented Lagrangian method (ALM)is used to handle these constrained optimization problems by convertingthem into unconstrained optimization problems. Three examples from theliterature are considered and transformed into their deterministic form usingthe chance-constrained technique. The transformed problems are solved usingGSK algorithm and the results are compared with eight other state-of-the-artmetaheuristic algorithms. The obtained results are also compared with theoptimal global solution and the results quoted in the literature. To investigatethe performance of the GSK algorithm on a real-world problem, a solidstochastic fixed charge transportation problem is examined, in which theparameters of the problem are considered as random variables. The obtainedresults show that the GSK algorithm outperforms other algorithms in termsof convergence, robustness, computational time, and quality of obtainedsolutions.
