Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs

Abstract

The honey-bee mating process is considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is applied to extract the linear monthly operation rules of reservoirs for both irrigation and hydropower purposes. The release rules for each month are considered as a linear function of the reservoir past-month-end storage as well as current monthly inflow to the reservoir. In such a case, the decision variables are 36 for each problem and are set so that water supply deficits are minimized. In both irrigation and hydropower purposes, 60-480 months are considered and results are compared to those from the nonlinear programming solver of the LINGO 8.0 software. The approach and the rules tend to be very promising and denote the capability of the proposed HBMO algorithm in solving reservoir operation problems. Furthermore, the results indicated that, by using the near-optimal solution from the HBMO as a starting point for the NLP solver, the obtained objective function value was enhanced significantly and a better local optimum was found.

Reservoir Optimization and Simulation Modeling: A Case Study

“…This model allows a user to quickly input model formulation, assess the correctness or appropriateness of the formulation based on the solution, quickly make minor modifications to the formulation, and repeat the process. Many researchers such as Bozorg Haddad et al (2008) and Montazar et al (2010) have applied LINGO to arrive at an optimal allocation plan of surface and ground water for various types of hydrosystems.…”

Reservoir operation management by optimization and stochastic simulation

“…This model allows a user to quickly input the model formulation, assess the correctness or appropriateness of the formulation based on the solution, quickly make minor modifications to the formulation, and repeat the process. Many researchers, such as Bozorg Haddad et al (2008) and Montazar et al (2010), applied LINGO to evolve an optimal allocation plan of surface and ground water for various hydrosystem types. In another study, Ziaei et al (2012) combined LINGO and the Hydrologie Engineering Center's Reservoir System Simulation (HECResSim) models to determine monthly operating rules for the Zayandeh-Rud Reservoir system in the central part of Iran.…”

Fundamentos de otimização por inteligência de enxames: uma visão geral

“…Embora os algoritmos baseados em colônias de abelhas sejam relativamente recentes, algumas aplicações têm sido propostas na literatura, tais como a solução do problema do caixeiro viajante (Lucic & Teodorovic, 2003;Wong et al, 2008), determinção de estratégias de aterrisagem em veículos aéreos não-tripulados (Chahl et al, 2004), despacho econômico de energia (Qin et al, 2004), sintonização de controladores (Azeem & Saad, 2004;Leivislö & Joensuu, 2006;Pham et al, 2007b;Serapião, 2009), escalonamento job shop (Chong et al, 2006), otimização de pesos de redes neurais (Pham et al, 2006b,c,d), otimização de máquinas de vetores suportes (Pham et al, 2007e), direção de robôs (Curkovic & Jerbic, 2007), projeto de células de manufatura (Pham et al, 2007a), projeto de junção de soldas (Pham et al, 2007d), particionamento e escalonamento (Koudil et al, 2007;Banerjee et al 2008), alocação de recursos (Quijano & Passino, 2007), problema de atribuição (Baykasoglu et al, 2007), planejamento de transporte (Wedde, et al, 2007), calibração de modelos hidrológicos (Barros et al, 2007), determinação de conformação de proteínas (Bahamish et al, 2008), codificação de DNA , problema de roteamento de veículos (Marinakis et al, 2008b) e de locações (Marinakis et al, 2008b), análise de clusters (Fathian & Amiri, 2008), extração de regras (Bozorg Haddad et al, 2008), aprendizagem de cinemática inversa de robôs (Pham et al, 2008), projeto de filtros digitais (Karaboga, 2009).…”