Download Agent-Based Evolutionary Search by Ruhul A. Sarker, Tapabrata Ray PDF

By Ruhul A. Sarker, Tapabrata Ray

ISBN-10: 3642134246

ISBN-13: 9783642134241

The functionality of Evolutionary Algorithms should be more suitable through integrating the concept that of brokers. brokers and Multi-agents can convey many fascinating gains that are past the scope of conventional evolutionary strategy and studying.

This booklet offers the state-of-the paintings within the concept and perform of Agent dependent Evolutionary seek and goals to extend the attention in this potent expertise. This comprises novel frameworks, a convergence and complexity research, in addition to real-world functions of Agent established Evolutionary seek, a layout of multi-agent architectures and a layout of agent communique and studying technique.

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Because an agent lattice may contain multiple copies of one or more agents, the number of elements in L is ⎛ | S | + Lsize × Lsize − 1⎞ | L |= ⎜ ⎟. Lsize × Lsize ⎝ ⎠ Multi-Agent Evolutionary Model for Global Numerical Optimization 23 Suppose that the energy of an agent lattice, L, is equal to Energy(L), which is determined by, Energy ( L) = max {Energy ( Li , j ) i , j = 1, Thus ∀L ∈ L, E E , Lsize } (20) ≤ Energy ( L) ≤ E 1 . Therefore, L can be partitioned into a col- lection of nonempty subsets {Li i = 1, 2, ,| E |} , where { Li = L L ∈ L and Energy ( L) = E i ∑ |E | i =1 | Li |=| L |; Li ≠ ∅, ∀i ∈ {1, 2, Li ∩ Lj = ∅, ∀i ≠ j; } ,| E |}; |E | ∪ i =1 Li = L (21) (22) L1 consists of all the agent lattices whose energies are E1.

Liu, W. Zhong, and L. 20 5 Conclusions Based on multi-agent systems, a new numerical optimization algorithm, MAGA, has been proposed. MAGA was tested on 10 benchmark functions and compared with four famous algorithms, FEP [19], OGA/Q [14], BGA [20] and AEA [13]. The experiments on functions with 30 dimensions and 20~1000 dimensions indicated that MAGA outperforms the four algorithms. In order to study the scalability of MAGA along the problem dimension, MAGA was used to optimize the 10 functions with 1000~10,000 dimensions.

In Multi-Agent Evolutionary Model for Global Numerical Optimization 29 addition, because ε is assigned to 10-4 in the termination criterion, small standard deviations are obtained for all functions at all selected dimensions. BGA [20] and AEA [13] are also tested on f1-f4 with 20, 100, 200, 400, 1000 dimensions. In [20] and [13], the termination criteria of BGA and AEA were the same as that of MAGA, but they used ε = 10 −4 for f1, ε = 10−1 for f2, and ε = 10−3 for f3 and f4. Therefore, a comparison is made between BGA, AEA, and MAGA, which is shown in Table 3.

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