A Memetic Multi-objective Immune Algorithm for Reservoir Flood Control Operation

Qi, Yutao; Li, Bao; Sun, Yingying; Luo, Junyi; Miao, Qiguang

doi:10.1007/s11269-016-1317-7

Cited by 25 publications

(10 citation statements)

References 41 publications

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“…Multi-objective optimisation refers to the process of simultaneously optimising two or more conflicting objectives subject to some given constraints. Multi-objective immune algorithm simulate the antigen-antibody reaction of the immune system in mammals (Qi et al, 2016(Qi et al, , 2015. In particular, the antigen and the antibody are equivalent to the objective function and the feasible solution for an optimisation problem (Lin et al, 2015;Liang et al, 2015).…”

Section: The Proposed Node Deployment Methods Based On Multi-objectivementioning

confidence: 99%

Wireless sensor network node deployment based on multi-objective immune algorithm

2018

IJIPT

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Wireless sensor network (WSN) is made up of a large number of low-cost wireless sensor nodes, which can collect all types of data in its lifetime. Node deployment of WSN is a NP complete problem, and it can significantly influence the network coverage and energy consumption in WSN. In this paper, we try to exploit multi-objective immune algorithm to solve the node deployment problem in WSN. The proposed node deployment algorithm aims to maximise the degree of network coverage and minimise the energy consumption. In the proposed multi-objective immune algorithm, each antibody refers to a candidate solution in node deployment process, and the antibodies are randomly initialised with a specific range. In addition, we design a fitness function by two ranking modes. Finally, experiments are conducted to test the performance of the proposed algorithm. Particularly, we use some typical performance metric in this experiment, such as coverage degree, energy consumption, and maximum moved distance. Experimental results demonstrate that the proposed can both enhance the network coverage degree and reduce the energy consumption by minimising the moved distance.

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Section: The Proposed Node Deployment Methods Based On Multi-objectivementioning

confidence: 99%

Wireless sensor network node deployment based on multi-objective immune algorithm

2018

IJIPT

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“…They concluded that the MOIA-PS achieved more non-dominated solutions that were scattered in the preferred area of the Pareto front and successfully reduced the flood peak to no more than 14,000 m 3 /s. In another study, Qi et al (2016) developed a variety of the IA inspired by the memetic algorithm, named multi-objective immune algorithm 2 (M-NNIA2), and solved seven test problems. They made a comparison between the M-NNIA2, NNIA2, NSGA-II, NNIA, and MOEA/D and reported that M-NNIA2 achieved a representative set of best tradeoff scheduling plans, and M-NNIA2 converged to the objective function after 2000 fitness evaluations when the other algorithms had not converged yet.…”

Section: The Immune Algorithmmentioning

confidence: 99%

“…This algorithm decomposes a multiobjective optimization problem into scalar subproblems and optimizes them simultaneously. Two papers applied this algorithm to optimize single reservoir with multiple objectives (Ma et al 2015;Qi et al 2016). Eight test problems were used, and a comparison between the MOEA/D and other EAs was made in both papers.…”

Section: The Multi-objective Evolutionary Algorithm Based On Decomposmentioning

confidence: 99%

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2019

Environ Monit Assess

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Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

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“…The reason we employ RS method for optimizing the large cooling tower’s geometry is that RS method has the advantage of providing the intuitive three-dimensional (3D) plot for identifying the optimization domain globally. Although there are many other optimization methods for use, for example, genetic algorithm (Ma et al, 2019), differential evolution algorithm (Wang et al, 2019), immune algorithm (Qi et al, 2016), bee colony algorithm (Najimi et al, 2019), and neural network (Suryanita et al, 2019), none of them has RS method’s advantage of avoiding local optimization.…”

Section: Geometry Optimization For the Cooling Tower Based On The Winmentioning

confidence: 99%

Wind-induced internal pressures on large cooling towers

Zhao

et al. 2019

Advances in Structural Engineering

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Effects of wind-induced internal pressures on the cooling tower’s structural performances are as significant as those of wind-induced external pressures. However, comparing to wind-induced external pressures, limited research focuses on wind pressures on the internal surfaces of large cooling towers. To fill up the scientific void, numerical analyses, physical model tests, and analytical studies are undertaken in this article. It is demonstrated that the draught ventilation ratio (i.e. the total area of the openings on the stuffing layer divided by the area of the stuffing layer) is the dominant factor for wind-induced internal pressures on large cooling towers, and 15% draught ventilation ratio can be regarded as the most unfavorable case. Besides, it is revealed that the theoretical formulation of the internal pressure on a single-cell building with a dominant opening and background porosity proposed by some other researchers can be applied to the case of a cooling tower subjected to strong winds. Using the validated theoretical formulation, the geometry of a large cooling tower is optimized with regard to the most favorable wind-induced internal pressure. The findings of this article are helpful for improving the current Chinese Code that governs the design of cooling towers.

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A Memetic Multi-objective Immune Algorithm for Reservoir Flood Control Operation

Cited by 25 publications

References 41 publications

Wireless sensor network node deployment based on multi-objective immune algorithm

Wireless sensor network node deployment based on multi-objective immune algorithm

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Wind-induced internal pressures on large cooling towers

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