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1.
Zhong et al. (2004 [IEEE Trans. on Systems, Man and Cybernetics (Part B), 34: 1128–1141]) proposed the multiagent genetic algorithm (MAGA) in their publication titled “A multiagent genetic algorithm for global numerical optimization”. The MAGA exploits the known characteristics of some benchmark functions to achieve outstanding results. For example, the MAGA exploits the fact that all variables have the same numerical value at the global optimum and the same upper and lower bounds to solve several 100 dimensional and 1000 dimensional benchmark problems with high precision requiring on average 7000 and 16,000 function evaluations respectively. In this paper, we evaluate the performance of the MAGA experimentally1 and demonstrate that the performance of the MAGA significantly deteriorates when the relative positions of the variables at the global optimal point are shifted with respect to the search ranges.  相似文献   

2.
An improved multi-agent genetic algorithm for numerical optimization   总被引:1,自引:0,他引:1  
Multi-agent genetic algorithm (MAGA) is a good algorithm for global numerical optimization. It exploited the known characteristics of some benchmark functions to achieve outstanding results. But for some novel composition functions, the performance of the MAGA significantly deteriorates when the relative positions of the variables at the global optimal point are shifted with respect to the search ranges. To this question, an improved multi-agent genetic algorithm for numerical optimization (IMAGA) is proposed. IMAGA make use of the agent evolutionary framework, and constructs heuristic search and a hybrid crossover strategy to complete the competition and cooperation of agents, a convex mutation operator and some local search to achieve the self-learning characteristic. Using the theorem of Markov chain, the improved multi-agent genetic algorithm is proved to be convergent. Experiments are conducted on some benchmark functions and composition functions. The results demonstrate good performance of the IMAGA in solving complicated composition functions compared with some existing algorithms.  相似文献   

3.
For the low optimization precision and long optimization time of genetic algorithm, this paper proposed a multi-population agent co-genetic algorithm with chain-like agent structure (MPAGA). This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition and orthogonal crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. In order to verify the optimization precision of this algorithm, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPAGA has higher optimization precision and shorter optimization time than MAGA.  相似文献   

4.
The MAGA is an effective algorithm used for global numerical optimization problems. Drawbacks, however, still existed in the neighborhood selection part of the algorithm. Based on the social cooperate mechanism of agents, an effective neighborhood construction mode is proposed. This mode imports an acquaintance net which describes the relation of agents, and uses that to construct the local environment (neighborhood) for agents. This strategy makes the new mode more reasonable than that of MAGA. The Multi-Agent Social Evolutionary Algorithm (MASEA) based on this construction mode is introduced, and some standard testing functions are tested. In the first experiments, two dimensional, 30 dimensional and 20-1000 dimensional functions are tested to prove the effectiveness of this algorithm. The experimental results show MASEA can find optimal or close-to-optimal solutions at a low computational cost, and its solution quality is quite stable. In addition, the comparative results indicate that MASEA performs much better than the CMA-ES and MAGA in both quality of solution and computational complexity. Even when the dimensions reach 10,000, the performance of MASEA is still good.  相似文献   

5.
This paper systematically proposed a multi-population agent co-genetic algorithm with double chain-like agent structure (MPATCGA) to solve the problem of the low optimization precision and long optimization time of simple genetic algorithm in terms of two coding strategy. This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition, and improved crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. Besides, the size of each sub-population is adaptive. The characteristic is very competitive when dealing with imbalanced workload. In order to verify the optimization precision of this algorithm with binary coding, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPATCGA has higher optimization precision and shorter optimization time than MAGA. Besides, in order to show the optimization performance of MPATCGA with real coding, the authors used it for feature selection problems as optimization algorithm and compared it with some other well-known GAs. The experimental results show that MPATCGA has higher optimization precision (feature selection precision). In order to show the performance of the adaptability of size of sub-populations, MPATCGA with sub-populations with same size and MPATCGA with sub-populations with different size are compared. The experimental results show that when the workload on different sub-populations becomes not same, the adaptability will adaptively change the size of different sub-population to obtain precision as high as possible.  相似文献   

6.
多智能体遗传算法用于线性系统逼近   总被引:14,自引:3,他引:14  
提出了一种新的参数优化方法--多智能体遗传算法,来求解线性系统逼近问题. 该方法中每个智能体代表一个候选解,即搜索空间中的一个实值向量.所有智能体生存在一 个网格状的环境中,且每个智能体占据一个格点不能移动.为了增加能量,它们将与其邻域 进行合作或竞争,也可以利用自身的知识.因此,设计了4个进化算子来模拟智能体间的竞 争、合作、自学习等行为.该方法利用这些智能体与智能体间的相互作用来达到优化逼近模 型中参数的目的;此外,还采用了一种动态扩展搜索空间的方法以解决算法所需的搜索空间 难以确定的问题.实验中,利用一个稳定和一个非稳定的线性系统逼近问题来验证算法的性 能,并与两种新近提出的方法作了比较.结果表明,该文方法优于其它方法,能够用较少的计 算量找到高质量的逼近模型,具有良好的性能和实际应用价值.  相似文献   

7.
将智能体引入到遗传算法构成一个局部环境,可有效保持种群的多样性从而获得优良的优化性能。但是这个局部环境的空间维数一直未得到研究。根据智能体遗传算法的工作原理,空间维数越小,越能避免过早收敛现象发生。基于此,提出一种维数为1的链式智能体遗传算法(CAGA),并针对函数优化问题将其与文献[4]提出的维数为2的网络式智能体遗传算法(MAGA)进行了比较。实验采用了多个多维复杂函数进行优化实验,结果表明,该遗传算法比二维网格式遗传算法可获得更优的优化结果。  相似文献   

8.
Multiagent based differential evolution approach to optimal power flow   总被引:1,自引:0,他引:1  
This paper proposes a new differential evolution approach named as multiagent based differential evolution (MADE) based on multiagent systems, for solving optimal power flow problem with non-smooth and non-convex generator fuel cost curves. This method integrates multiagent systems (MAS) and differential evolution (DE) algorithm. An agent in MADE represents an individual to DE and a candidate solution to the optimization problem. All agents live in a lattice like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbors and it can also use knowledge. Making use of these agent-agent interaction and DE mechanism, MADE realizes the purpose of minimizing the value of objective function. MADE applied to optimal power flow is evaluated on 6 bus system and IEEE 30 bus system with different generator characteristics. Simulation results show that the proposed method converges to better solutions much faster than earlier reported approaches.  相似文献   

9.
This paper presents a novel evolutionary algorithm entitled Dynamic Partition Search Algorithm (DPSA) for global optimization problems with continuous variables. The DPSA is a population-based stochastic search algorithm in nature, which mainly consists of initialization process and evolution process. In the initialization process, the DPSA randomly generates an initial population of members from a specific search space and finds a leader. In the evolution process, the DPSA applies two groups to balance exploration ability and exploitation ability, in which one group is in charge of exploring new region via a dynamic partition strategy, and the other group relies on Cauchy distributions to exploit the region around the best member. Later, numerical experiments are conducted for 24 classical benchmark functions with 100, 1000 or even 10000 dimensions. And the performance of the proposed DPSA is compared with a state-of-the-art cooperative coevolving particle swarm optimization (CCPSO2), and two existing differential evolution (DE) algorithms. The experimental results show that DPSA has a better performance than the algorithms used for comparison, especially for high dimensional optimization problems. In addition, the numerical computational results also demonstrate that the DPSA has good scalability, and it is an effective evolutionary algorithm for solving large-scale global optimization problems.  相似文献   

10.
Based on our previous works, multiagent systems and evolutionary algorithms (EAs) are integrated to form a new algorithm for combinatorial optimization problems (CmOPs), namely, MultiAgent EA for CmOPs (MAEA-CmOPs). In MAEA-CmOPs, all agents live in a latticelike environment, with each agent fixed on a lattice point. To increase energies, all agents compete with their neighbors, and they can also increase their own energies by making use of domain knowledge. Theoretical analyses show that MAEA-CmOPs converge to global optimum solutions. Since deceptive problems are the most difficult CmOPs for EAs, in the experiments, various deceptive problems with strong linkage, weak linkage, and overlapping linkage, and more difficult ones, namely, hierarchical problems with treelike structures, are used to validate the performance of MAEA-CmOPs. The results show that MAEA-CmOP outperforms the other algorithms and has a fast convergence rate. MAEA-CmOP is also used to solve large-scale deceptive and hierarchical problems with thousands of dimensions, and the experimental results show that MAEA-CmOP obtains a good performance and has a low computational cost, which the time complexity increases in a polynomial basis with the problem size.   相似文献   

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