The Suitability of Particle Swarm Optimisation for Training Neural Hardware

Braendler, David; Hentdlass, Tim

doi:10.1007/3-540-48035-8_19

Cited by 11 publications

(10 citation statements)

References 7 publications

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“…In the same vein, PSO has been used in diverse application areasdynamic economic dispatch problems [12], quadratic assignment problems [24], pole shape optimization [10], and neural networks training [9,21,35]. Despite the popularity and application of both algorithms in the aforementioned areas of science and engineering, they are almost nonexistent in the well placement optimization problem domain, but for [20] and [47].…”

Section: Introductionmentioning

confidence: 99%

Hybrid differential evolution and particle swarm optimization for optimal well placement

2012

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There is no gainsaying that determining the optimal number, type, and location of hydrocarbon reservoir wells is a very important aspect of field development planning. The reason behind this fact is not farfetched—the objective of any field development exercise is to maximize the total hydrocarbon recovery, which for all intents and purposes, can be measured by an economic criterion such as the net present value of the reservoir during its estimated operational life-cycle. Since the cost of drilling and completion of wells can be significantly high (millions of dollars), there is need for some form of operational and economic justification of potential well configuration, so that the ultimate purpose of maximizing production and asset value is not defeated in the long run. The problem, however, is that well optimization problems are by no means trivial. Inherent drawbacks include the associated computational cost of evaluating the objective function, the high dimensionality of the search space, and the effects of a continuous range of geological uncertainty. In this paper, the differential evolution (DE) and the particle swarm optimization (PSO) algorithms are applied to well placement problems. The results emanating from both algorithms are compared with results obtained by applying a third algorithm called hybrid particle swarm differential evolution (HPSDE)—a product of the hybridization of DE and PSO algorithms. Three cases involving the placement of vertical wells in 2-D and 3-D reservoir models are considered. In two of the three cases, a max-mean objective robust optimization was performed to address geological uncertainty arising from the mismatch between real physical reservoir and the reservoir model. We demonstrate that the performance of DE and PSO algorithms is dependent on the total number of function evaluations performed; importantly, we show that in all cases, HPSDE algorithm outperforms both DE and PSO algorithms. Based on the evidence of these findings, we hold the view that hybridized metaheuristic optimization algorithms (such as HPSDE) are applicable in this problem domain and could be potentially useful in other reservoir engineering problems

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Section: Introductionmentioning

confidence: 99%

Hybrid differential evolution and particle swarm optimization for optimal well placement

2012

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“…The optimization technique was originally aimed at studying social network structure and now it is widely used in computational intelligence, industrial optimization, computer science and engineering research. PSO is also used in training neural networks (Braendler and Hendtlass 2002), solving dynamic economic dispatch problems (Chakrabarti et al 2006), pole shape optimization problems (Brandstatter and Baumgartner 2002), quadratic assignment problems (Gong and Tuson 2008) and many other high-dimensional optimization problems in the real world with multiple optima.…”

Section: Swarm Intelligence For Finding E(s2)-optimal Ssdsmentioning

confidence: 99%

Optimizing Two-Level Supersaturated Designs Using Swarm Intelligence Techniques

et al. 2016

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Supersaturated designs (SSDs) are often used to reduce the number of experimental runs in screening experiments with a large number of factors. As more factors are used in the study, the search for an optimal SSD becomes increasingly challenging because of the large number of feasible selection of factor level settings. This paper tackles this discrete optimization problem via an algorithm based on swarm intelligence. Using the commonly used E(s2) criterion as an illustrative example, we propose an algorithm to find E(s2)–optimal SSDs by showing that they attain the theoretical lower bounds in Bulutoglu and Cheng (2004) and Bulutoglu (2007). We show that our algorithm consistently produces SSDs that are at least as efficient as those from the traditional CP exchange method in terms of computational effort, frequency of finding the E(s2)-optimal SSD and also has good potential for finding D3–, D4– and D5–optimal SSDs.

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“…Braendler and Hendtlass proposed a variation where particles move toward neighbouring particles that have found a good solution [2]. The proposed new algorithm, modelled on the behaviour of locusts and crickets, extends this idea to the multi-objective optimisation problem by making comparison between neighbouring particles based on Pareto dominance, and adding a corresponding repulsion to the previously suggested attraction.…”

Section: Introductionmentioning

confidence: 96%

LoCost: A spatial social network algorithm for multi-objective optimisation

Lewis

2009

2009 IEEE Congress on Evolutionary Computation

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Particle Swarm Optimisation (PSO) is increasingly being applied to optimisation of problems in engineering design and scientific investigation. While readily adapted to singleobjective problems, its use on multi-objective problems is hampered by the difficulty of finding effective means of guiding the swarm in the presence of multiple, competing objectives. This paper suggests a novel approach to this problem, based on an extension of the concepts of spatial social networks using a model of the behaviour of locusts and crickets. Comparison is made between neighbouring particles based on Pareto dominance, and a corresponding repulsion between particles added to previously suggested attractive forces. Computational experiments demonstrate that the new, spatial, social network optimisation algorithm can provide results comparable to a conventional MOPSO algorithm, and improved coverage of the Pareto-front.

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The Suitability of Particle Swarm Optimisation for Training Neural Hardware

Cited by 11 publications

References 7 publications

Hybrid differential evolution and particle swarm optimization for optimal well placement

Hybrid differential evolution and particle swarm optimization for optimal well placement

Optimizing Two-Level Supersaturated Designs Using Swarm Intelligence Techniques

LoCost: A spatial social network algorithm for multi-objective optimisation

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