Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with geneticalgorithm (GA). dualpopulation GA (DPGA) helps to provide additional ...
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Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with geneticalgorithm (GA). dualpopulation GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA). (C) 2014 Elsevier Inc. All rights reserved.
This paper conducts a comparative study between an improved variants of geneticalgorithm (GA) and a swarm intelligence algorithm (SIA), which are the dual population genetic algorithm (DPGA) and Artificial Bee Colony...
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ISBN:
(纸本)9781509040902
This paper conducts a comparative study between an improved variants of geneticalgorithm (GA) and a swarm intelligence algorithm (SIA), which are the dual population genetic algorithm (DPGA) and Artificial Bee Colony (ABC) algorithm. DPGA is a multi-populationgeneticalgorithm (MPGA) that implements two population such as the main population and a complementary population. Since the added population has a totally different fitness function, it is preserved to supply sufficient diversity to the main population by crossover operation. However DPGA employs a dynamic strategy to maintain an appropriate distance between two populations for maintaining diversity. On the contrary ABC, a simple but exceptional derivative of SIA, applies division of labor in single population of artificial bees and allocates some of them to exploration while the others to exploitation. DPGA has its own techniques to sustain the significant balance between exploration vs. exploitation. Thus many such analytical comparisons between DPGA and ABC are the center of attention of this paper. Experiments are conducted on seven benchmark functions using ABC and results are compared with DPGA. The results demonstrate that DPGA performs well for some of the functions but by considering the result of mean absolute error, ABC performs far better than DPGA.
Small satellites have the outstanding advantages of flexible reconfiguration and strong system robustness through large-scale network operation, which has attracted attention at domestic and overseas in recent years. ...
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Small satellites have the outstanding advantages of flexible reconfiguration and strong system robustness through large-scale network operation, which has attracted attention at domestic and overseas in recent years. However, how to solve the scheduling problem in large-scale satellite constellation/ cluster production is always the key to increasing the volume production of satellites. In this paper, the existing production line framework and the critical technologies of intelligent manufacturing are analyzed, and the intelligent production line flow is proposed. Based on the establishment of the job shop scheduling (JSP) model, the Interference of multi-model scheduling is classified, and by improving the dynamic scheduling strategy of the dual population genetic algorithm, we solve the multi-model scheduling problem. The simulation results show that the scheduling scheme can minimize the influence of interference events on the schedule, which proves the superiority and effectiveness of the scheduling strategy.
This paper conducts a comparative study between an improved variants of geneticalgorithm (GA) and a swarm intelligence algorithm (SIA), which are the dual population genetic algorithm (DPGA) and Artificial Bee Colony...
详细信息
ISBN:
(纸本)9781509040919
This paper conducts a comparative study between an improved variants of geneticalgorithm (GA) and a swarm intelligence algorithm (SIA), which are the dual population genetic algorithm (DPGA) and Artificial Bee Colony (ABC) algorithm. DPGA is a multi-populationgeneticalgorithm (MPGA) that implements two population such as the main population and a complementary population. Since the added population has a totally different fitness function, it is preserved to supply sufficient diversity to the main population by crossover operation. However DPGA employs a dynamic strategy to maintain an appropriate distance between two populations for maintaining diversity. On the contrary ABC, a simple but exceptional derivative of SIA, applies division of labor in single population of artificial bees and allocates some of them to exploration while the others to exploitation. DPGA has its own techniques to sustain the significant balance between exploration vs. exploitation. Thus many such analytical comparisons between DPGA and ABC are the center of attention of this paper. Experiments are conducted on seven benchmark functions using ABC and results are compared with DPGA. The results demonstrate that DPGA performs well for some of the functions but by considering the result of mean absolute error, ABC performs far better than DPGA.
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