Future Generation Computer Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing
Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing
Integrated Computer-Aided Engineering
Artificial Intelligence with Uncertainty
Artificial Intelligence with Uncertainty
A distributed learning algorithm for particle systems
Integrated Computer-Aided Engineering
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Convergence analysis of canonical genetic algorithms
IEEE Transactions on Neural Networks
A parallel genetic/neural network learning algorithm for MIMD shared memory machines
IEEE Transactions on Neural Networks
A wavelet-based particle swarm optimization algorithm for digital image watermarking
Integrated Computer-Aided Engineering - Anniversary Volume: Celebrating 20 Years of Excellence
Causally-guided evolutionary optimization and its application to antenna array design
Integrated Computer-Aided Engineering
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The stagnation phenomena regularly happen in solving complex optimization problems by a class of computational intelligence algorithms which lack theoretical guidance for their parameters setting. The search characteristics of these algorithms are analyzed in this paper, based on which an adaptive computational intelligence optimization algorithm called cloud drops algorithm is proposed by adopting a cloud model to express the randomness and fuzziness of its search process. The cloud drops algorithm is characterized by representing, mining and recreating the uncertain knowledge about its search process for the optimal solution. None of search parameters are predefined in the proposed algorithm and, whatever the initial solution set is, the whole system can adaptively approach to the global optimum. Based on the theory of stochastic processes, the almost sure convergence of the proposed algorithm is proved under certain conditions by introducing a martingale approach into traditional Markov Chain analysis. Two benchmark problems are tested with the proposed algorithm and the other two existing algorithms as a comparison. The results show that the proposed algorithm has faster convergence speed, better self-adaptability, and stronger ability to deal with stagnation phenomena effectively.