Optimizing genetic algorithm strategies for evolving networks

Berryman, Matthew J.; Allison, Andrew; Abbott, Derek

doi:10.1117/12.548122

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Interesting recent studies have examined the tradeoffs between redundancy and pleiotropy [23], and centralized versus decentralized design [24], in complex networks. Finding the resistance between any two points on a complex network is tractable and builds upon early mesh-resistance techniques [25].…”

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confidence: 99%

Scaling in small-world resistor networks

et al. 2006

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We study the effective resistance of small-world resistor networks. Utilizing recent analytic results for the propagator of the Edwards-Wilkinson process on small-world networks, we obtain the asymptotic behavior of the disorder-averaged two-point resistance in the large system-size limit. We find that the small-world structure suppresses large network resistances: both the average resistance and its standard deviation approaches a finite value in the large system-size limit for any non-zero density of random links. We also consider a scenario where the link conductance decays as a power of the length of the random links, l −α . In this case we find that the average effective system resistance diverges for any non-zero value of α.

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confidence: 99%

Scaling in small-world resistor networks

et al. 2006

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“…Besides, the classic optimization methods are prone to get trapped in local minima and not reach the global optimum [15] when solving complex multimodal optimization problems of array weight extraction, resulting in a suboptimum beamforming performance. In addition, most of MH algorithms are population-based optimization techniques which require long execution times to converge, specifically when solving large-scale complex ABF engineering problems [16, 17], and the complexity implementing the algorithms would also result in huge cost and hardware resources.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a novel Fibonacci branch search optimizer for the design of the low sidelobe and deep nulling adaptive beamformer

Zhang

Zeng

2020

Int. J. Microw. Wireless Technol.

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In this work, we proposed an adaptive beamformer based on a novel heuristic optimization algorithm. The novel optimization technique inspired from Fibonacci sequence principle, designated as Fibonacci branch search (FBS), used new tree's branches fundamental structure and interactive searching rules to obtain the global optimal solution in the search space. The branch structure of FBS is selected using two types of multidimensional points on the basis of shortening fraction formed by Fibonacci sequence; in this mode, interactive global and local searching rules are implemented alternately to obtain the optimal solutions, avoiding stagnating in local optimum. The proposed FBS is also used here to construct an adaptive beamforming (ABF) technique as a real-time implementation to achieve near-optimal performance for its simplicity and high convergence rate, then, the performance of the FBS is compared with the five typical heuristic optimization algorithms. Simulation results demonstrate the superiority of the proposed FBS approach in locating the optimal solution with higher precision and reveal further improvement in the ABF performance.

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“…5,6 They allow us to capture the dynamic of the telecommunications network as data links, servers, and clients are added to the network, removed from it, fail, or get repaired. Thus, the modification of the problem specifications, constraints, and/or objective functions do not require the optimization process to be restarted as evolutionary algorithms can adapt to the changes.…”

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confidence: 99%

“…The initial adjacency matrix A i of the graph G prior to applying Dijkstra's algorithm is compared with the adjacency matrix A d obtained from applying Dijkstra's algorithm to every node. 5,6 If a ij in A i is same as that in A d , a ij is entered into the adjacency matrix A m of the set of shortest paths. This is because the set of shortest paths gives the shortest path from node i to node j.…”

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confidence: 99%

Multi-objective evolutionary algorithm for investigating the trade-off between pleiotropy and redundancy

Ong

Berryman

et al. 2005

Complex Systems

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The trade-off between pleiotropy and redundancy in telecommunications networks is analyzed in this paper. They are optimized to reduce installation costs and propagation delays. Pleiotropy of a server in a telecommunications network is defined as the number of clients and servers that it can service whilst redundancy is described as the number of servers servicing a client. Telecommunications networks containing many servers with large pleiotropy are cost-effective but vulnerable to network failures and attacks. Conversely, those networks containing many servers with high redundancy are reliable but costly. Several key issues regarding the choice of cost functions and techniques in evolutionary computation (such as the modeling of Darwinian evolution, and mutualism and commensalism) will be discussed, and a future research agenda is outlined. Experimental results indicate that the pleiotropy of servers in the optimum network does improve, whilst the redundancy of clients do not vary significantly, as expected, with evolving networks. This is due to the controlled evolution of networks that is modeled by the steady-state genetic algorithm; changes in telecommunications networks that occur drastically over a very short period of time are rare.

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Optimizing genetic algorithm strategies for evolving networks

Cited by 3 publications

References 11 publications

Scaling in small-world resistor networks

Scaling in small-world resistor networks

Implementation of a novel Fibonacci branch search optimizer for the design of the low sidelobe and deep nulling adaptive beamformer

Multi-objective evolutionary algorithm for investigating the trade-off between pleiotropy and redundancy

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