Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used ...Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.展开更多
Knowledge acquisition Is the bottleneck of expert system. To solve this problem, KD (D&K), which is a comprehensive knowledge discovery process model coopersting both database and knowledge base, and related techno...Knowledge acquisition Is the bottleneck of expert system. To solve this problem, KD (D&K), which is a comprehensive knowledge discovery process model coopersting both database and knowledge base, and related technology are proposed. Then based on KD (D&K) and related technology, the new construction of Expert System based on Knowledge Discovery (ESKD) Is proposed. As the key knowledge acqulsltlon component of ESKD, KD (D&K) Is composed of KDD* and KDK*. KDD*- the new process model based on double bases cooperating mechanism; KDK*- the new process model based on double-basis fusion mechanism are Introduced, respectively. The overall framework of ESKD Is proposed. Some sub-systems and dynamic knowledge base system are discussed. Flnelly, the effectiveness and advantages of ESKD are tested In a real-world agriculture database. We hope that ESKD may be useful for the new generation of expert systems.展开更多
基金supported by Anhui Polytechnic University Introduced Talents Research Fund(No.2021YQQ064)Anhui Polytechnic University ScientificResearch Project(No.Xjky2022168).
文摘Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.
基金Supported by the National Natural Science Foundation of China (Grant No. 69835001)the Ministry of Education of China (Grant No. [2000] 175),the Science Foundation of Beijing (Grant No. 4022008).
文摘Knowledge acquisition Is the bottleneck of expert system. To solve this problem, KD (D&K), which is a comprehensive knowledge discovery process model coopersting both database and knowledge base, and related technology are proposed. Then based on KD (D&K) and related technology, the new construction of Expert System based on Knowledge Discovery (ESKD) Is proposed. As the key knowledge acqulsltlon component of ESKD, KD (D&K) Is composed of KDD* and KDK*. KDD*- the new process model based on double bases cooperating mechanism; KDK*- the new process model based on double-basis fusion mechanism are Introduced, respectively. The overall framework of ESKD Is proposed. Some sub-systems and dynamic knowledge base system are discussed. Flnelly, the effectiveness and advantages of ESKD are tested In a real-world agriculture database. We hope that ESKD may be useful for the new generation of expert systems.