Through analyzing the present genetic operators to solve the Job Shop Scheduling Problem, a inversion order number Genetic Algorithm is proposed. In view of the quality of the inversion order number, this algorithm me...
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The importance and necessity of emulating the federation load during simulating was analyzed ***,the special federation load emulation tool,federation load simulator (FLS),was designed and implemented,by which all of ...
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The importance and necessity of emulating the federation load during simulating was analyzed ***,the special federation load emulation tool,federation load simulator (FLS),was designed and implemented,by which all of the vital essential characteristics of a simulation could be tested,*** amount of federates,the joint/resigned speed of federate,the amount of object instances,the registered/deleted speed of instance in one single program,etc..The applications proved that FLS could provide a convenient,effective and adjustable simulation load testing environment during the procedure of run-time infrastructure(RTI)and interrelated tool federates researching,developing and performance ***,the FLS utilized all kinds of resources with high efficiency.
Artificial Physics Optimization (APO) algorithm inspired by natural physical forces is a population-based stochastic algorithm based on Physicomimetics framework. In this paper, an extended APO (EAPO) algorithm is pre...
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As a novel population-based optimization technique, the artificial physics optimization (APO) algorithm inspired by physics is presented recently[1-4]. Although it is characteristic of rapid convergence speed, it also...
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Synthesis of inorganic nanostructures with specific size and well defined morphologies has attracted considerable attention due to their superior electrical, optical, magnetic, and chemical properties. Up to now, vari...
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Synthesis of inorganic nanostructures with specific size and well defined morphologies has attracted considerable attention due to their superior electrical, optical, magnetic, and chemical properties. Up to now, various kinds of metal oxide, sulfide, and hydrate with controlled hierarchical and complex morphologies have been successfully synthesized.
This paper presents an improved particle swarm optimization (IPSO) to solve constrained optimization problems, which handles constraints based on certain feasibility-based rules. A turbulence operator is incorporated ...
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This paper presents a general framework of physics-inspired method named artificial physics optimization (APO) Algorithm, a population-based, stochastic for multidimensional search and optimization. APO invokes a grav...
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This paper presents a general framework of physics-inspired method named artificial physics optimization (APO) Algorithm, a population-based, stochastic for multidimensional search and optimization. APO invokes a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move particles toward local and global optima. APO's particles (solutions to the optimization problem) are treated as physical individuals, each individual has a mass, position and velocity. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. Responding to virtual forces, APO's individuals move toward other particles with larger ¿masses¿ (better fitnesses) and away from lower mass particles (worse fitnesses). Each individual attracts all others whose mass is lower, and repels all others whose mass is greater. The individual with the greatest mass (¿best¿ individual) attracts all other individuals, and it is neither attracted to nor repelled by all the others. The attraction-repulsion rule causes APO's population to search regions of the decision space with better fitnesses. Experimental simulations show that APO is tested against several benchmark functions with better results.
Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some case...
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Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some cases, therefore, in this paper, a technique, dynamic step length, is incorporated into the structure of stochastic particle swarm optimization aiming to further improve the performance. In this modification, each particle will adjust its velocity according to its performance. In other words, if it finds a better region, it will make a local search, otherwise, a global search pattern is given. By the way, to combining the advantages between the standard version (with better exploitation capability) and the stochastic version (with better exploration capability), the first half period is used with the standard version incorporated with dynamic step length, while in later generations, the stochastic version with dynamic step length is used to escape from a local optimum. simulation results show this strategy may provide well balance between exploration and exploitation capabilities, and improve the performance significantly.
Particle swarm optimization (PSO) is a new robust swarm intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the compu...
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Particle swarm optimization (PSO) is a new robust swarm intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the computational efficiency, there are still many works need to do. Inspired by evolutionary programming theory, this paper proposes a self-adaptive particle swarm optimization in which the velocity threshold dynamically changes during the course of a simulation, and two further techniques are designed to avoid badly adjusted by the self-adaption. Six benchmark functions are used to testify the new algorithm, and the results show the new adaptive PSO clearly leads to better performance, although the performance improvements were found to be dependent on problems.
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