This paper proposes an improved dynamical evolutionary algorithm (IDEA) based on Multi-parent *** preventing premature convergence effectively and keeping the population in good distribution,the new algorithm makes fu...
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This paper proposes an improved dynamical evolutionary algorithm (IDEA) based on Multi-parent *** preventing premature convergence effectively and keeping the population in good distribution,the new algorithm makes full use of Multi-parent Crossover,overcoming the disadvantage of big searching dead zone existed in conventional mutation *** numerical results show IDEA not only has good performance and a high degree of reliability while dealing with various complex problems,but also is superior to any other published results.
In this paper,a new dynamical evolutionary algorithm using chaos(CDEA) is proposed based on the statistical *** spread-spectrum characteristic of chaos, we generate multi-population in remote difference of the initial...
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In this paper,a new dynamical evolutionary algorithm using chaos(CDEA) is proposed based on the statistical *** spread-spectrum characteristic of chaos, we generate multi-population in remote difference of the initial *** to the selecting strategy of traditional dynamical evolutionary algorithm the best individual of the multi-population is migrated into the other populations replacing the worst one of them affirmatively has the chance to *** algorithm presented in this paper by introducing chaos has bigger selective pressure, and can keep diversity of the *** order to verify the effectiveness of our algorithm,we apply CDEA to solve the typical numerical function minimization *** experimental results show that CDEA is fast and reliable.
In this paper, we proposed the Tracking dynamical evolutionary algorithm (TDEA) that can efficiently locate and track the optimal solution in a dynamically changing environment. In TDEA, the particle's structure i...
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In this paper, we proposed the Tracking dynamical evolutionary algorithm (TDEA) that can efficiently locate and track the optimal solution in a dynamically changing environment. In TDEA, the particle's structure is different from traditional DEA. Each particle's knowledge is applied an "evaporation constant" to gradually weaken the knowledge's validity. Through this mechanism, the knowledge of each particle will be gradually updated in a dynamically changing environment. Compared with the traditional DEA, TDEA can quickly converge to the area of the goal and maintain the shortest distance from the goal.
A dynamical multi-objective evolutionaryalgorithm (DMOEA) is proposed. It is the first study of the dynamical evolutionary algorithm (DEA) in multi-objective optimization problems. All individuals called as particles...
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ISBN:
(纸本)0819451819
A dynamical multi-objective evolutionaryalgorithm (DMOEA) is proposed. It is the first study of the dynamical evolutionary algorithm (DEA) in multi-objective optimization problems. All individuals called as particles in a population evolve through a new selection mechanism. We combine the selection mechanism in DEA and the elitists strategy in existing evolutionary multi-objective optimization algorithms in DMOEA. The performance of DMOEA has been analyzed in comparison with SPEA2. The experimental results show that DMOEA clearly outperforms SPEA2 for the whole benchmark set. Moreover, a better convergence is sometimes observed in DMOEA for some functions of the benchmark set. The numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.
We introduce a new dynamical evolutionary algorithm（DEA） based on the theory of statistical mechanics and investigate the reconstruction problem for the nonlinear dynamical systems using observation data. The conver...
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We introduce a new dynamical evolutionary algorithm（DEA） based on the theory of statistical mechanics and investigate the reconstruction problem for the nonlinear dynamical systems using observation data. The convergence of the algorithm is discussed. We make the numerical experiments and test our model using the two famous chaotic systems （mainly the Lorenz and Chen systems）. The results show the relatively accurate reconstruction of these chaotic systems based on observational data can be obtained. Therefore we may conclude that there are broad prospects using our method to model the nonlinear dynamical systems.
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