A Combined Global &amp; Local Search (CGLS) Approach to Global Optimization

García-Palomares, Ubaldo M.; González-Castaño, Francisco J.; Burguillo, Juan C.

doi:10.1007/s10898-005-3249-2

Cited by 29 publications

(42 citation statements)

References 25 publications

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“…In Sect. 3 we review the background and show that the problem fulfils the conditions in [7] to apply the advanced derivative-free algorithms in [6]. In Sect.…”

Section: Introductionmentioning

confidence: 94%

“…This paper has a two fold objective: (i) to adapt a non-monotone derivative-free optimization technique that has recently appeared in the literature [6] and (ii) to compare its performance with standard approaches. We formulate and solve a computationally appealing optimization model that fulfills a desired quality of service (QoS) in the coverage area.…”

Section: Introductionmentioning

confidence: 99%

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Outdoor WLAN planning via non-monotone derivative-free optimization: algorithm adaptation and case study

et al. 2007

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In this paper, we study the application of non-monotone derivative-free optimization algorithms to wireless local area networks (WLAN) planning, which can be modeled as an unconstrained minimization problem. We wish to determine the access point (AP) positions that maximize coverage in order to provide connectivity to static and mobile users. As the objective function of the optimization model is not everywhere differentiable, previous research has discarded gradient methods and employed heuristics such as neighborhood search (NS) and simulated annealing (SA). In this paper, we show that the model fulfills the conditions required by recently proposed non-monotone derivative-free (DF) algorithms. Unlike SA, DF has guaranteed convergence. The numerical tests reveal that a tailored DF implementation (termed "zone search") outperforms NS and SA.

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“…In Sect. 3 we review the background and show that the problem fulfils the conditions in [7] to apply the advanced derivative-free algorithms in [6]. In Sect.…”

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Outdoor WLAN planning via non-monotone derivative-free optimization: algorithm adaptation and case study

et al. 2007

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“…The agent has not too much time to explore the space of values of CreditLimit, since a delay in the convergence to the right value could cause throughput loses. In this framework, we consider that evolutive algorithms perform enough good and somehow simpler than other more sophisticated optimization techniques [17]. An evolutive algorithm considers a population that evolves on three phases: couple selection, crossover and sporadically mutation.…”

Section: Learning Creditlimitmentioning

confidence: 99%

A Multi-agent Approach to Resource Sharing Optimization in User Networks

Burguillo

Costa-Montenegro

González-Castaño

2006

Computational Science – ICCS 2006

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Abstract. In this paper, we evaluate the feasibility of multiagent control of resources to be shared in user networks. A user network is totally controlled by the users, both at application and transport level. One of the possible applications in these networks is peer-to-peer (P2P) file exchange sharing the "external" access to the Internet (set of links between the user network and the Internet). If a node cannot serve its demand with its own external link, it requests help from another node via the high-bandwidth internal user network. We model user nodes as agents to simulate and to evaluate a new agent-based distributed control scheme. The simulation results in this paper confirm that it is possible to improve resource sharing in user networks using agents that take decisions autonomously, from local information, and check that file exchange services offered to neighbour nodes do not surpass appropriate credit limits.

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“…Therefore, an approach that combines thestrengths of stochastic and deterministic optimization schemes but avoids their weaknesses is of much interest. For details on such an approach, see (Noel 2012;Yiu et al 2004;Garcia-Palomares et al 2006). In this section, a hybrid algorithm, which combines the strengths of a gradientbased optimization technique and ABC algorithm, will be presented.…”

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confidence: 99%

A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

Liu

et al. 2015

Optimization Methods, Theory and Applications

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Archie formula, which contains three fundamental parameters (a, m, n), is the basic equation to compute the water saturation in a clean or shaly formation. These parameters are known as Archie parameters. To identify accurately the water saturation for a given reservoir condition, it depends critically on the accurate estimates of the values of Archie parameters (a, m, n). These parameters are interdependent and hence it is difficult to identify them accurately. So we present a new hybrid global optimization technique, where a gradient-based method with BFGS update is combined with an intelligent algorithm called Artificial Bee Colony. This new hybrid global optimization technique has both the fast convergence of gradient descent algorithm and the global convergence of swarm algorithm. It is used to identify Archie parameters in carbonate reservoirs. The results obtained are highly satisfactory. To further test the effectiveness of the new hybrid global optimization method, it is applied to ten non-convex benchmark problems. The outcomes are encouraging. Keywords Archie parameters • Hybrid global optimization • ABC algorithm • Gradient-based method IntroductionAn accurate identification of oil reserve in either an undeveloped or a developed reservoir is a significant task for a petro-physicist and reservoir engineer. To calculate the hydrocarbon reserve in its formation, it is required to know the amount of the water saturation. Inaccurate calculation of the amount of the water saturation will lead to a large error in the estimation of the hydrocarbon reserve.Archie equation, which is the underlying foundation for analyzing water saturation in potential oil and gas zones, is commonly used in the calculation of the amount of the water saturation of a reservoir rock, and hence providing an estimate of the initial hydrocarbon reserve of the reservoir. The values of the input parameters of Archie water saturation model in a clean or shaly formation must be estimated as accurately as possible. From field experiments, it is observed that the values of the following three input parameters, the cementation exponent m, the saturation exponent n, and the tortuosity factor a, depend critically on the petro-physical properties of a given rock. Thus, these parameters will take different values for different fields. Furthermore, the values of a, m and n in Archie formula are interdependent. Therefore, it is of critically importance that the estimations of the values of the parameters a, m and n are done accurately (Mabrouk et al. 2013;Michael et al. 2013;Makar and Kamel 2012).There are several techniques available in the literature for estimating the values of Archie parameters a; m and n. A conventional technique is to determine n separately, independent of a and m. This approach may not be valid in real situations, as it may lead to large error in the estimation of the amount of the water saturation (Archie 1942). In (Maute et al. 1992), a data analysis approach is proposed to determine the Archie parameters m; n an...

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A Combined Global & Local Search (CGLS) Approach to Global Optimization

Cited by 29 publications

References 25 publications

Outdoor WLAN planning via non-monotone derivative-free optimization: algorithm adaptation and case study

Outdoor WLAN planning via non-monotone derivative-free optimization: algorithm adaptation and case study

A Multi-agent Approach to Resource Sharing Optimization in User Networks

A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

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