A Honey Bee Algorithm To Solve Quadratic Assignment Problem

Assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. In this study, we solve quadratic assignment problem (QAP), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. It should be noted that any facility must be assign to only one location. In this paper, first of all, we have been described exact methods and heuristics, which are able to solve QAP; then we have been applied a meta-heuristic algorithm for it. QAP is a difficult problem and is in NP-hard class, so we have been used honey bee mating optimization (HBMO) algorithm to solve it.This method is new and have been applied and improved NP-hard problems. It’s a hybrid algorithm from Honey-Bee Mating system, simulated annealing and genetic algorithm.</p

Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al

Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka

This paper brings an in detail Genetic Algorithm (GA) based combinatorial optimization method used for the optimal design of the water distribution network (WDN) of Gurudeniya Service Zone, Sri Lanka. Genetic Algorithm (GA) mimics the survival of the fittest principle of nature to develop a search process. Methodology employs fuzzy combinations of pipe diameters to check their suitability to be considered as the cost effective optimal design solutions. Furthermore, the hydraulic constraints were implicitly evaluated within the GA itself in its aim to reaching the global optimum solution. Upon analysis, the results of this approach delivered agreeable design outputs. In addition, the comparison made between the results obtained by a previous study inspired by the Honey Bee Mating Optimization (HBMO) Algorithm and results obtained by the GA based approach, proves competency of GA for the optimal design of water distribution network in Gurudeniya Service Zone, Sri Lanka.Comment: Under a review of a journal. 20 pages. arXiv admin note: text overlap with arXiv:2209.1199

Optimization of Invasive Weed for Optimal Dimensions of Concrete Gravity Dams

Dam construction projects among the most extensive and most expensive projects are considered. It is always appropriate and optimal for such concrete structures to reduce the volume of concrete and consequently reduce construction costs is essential. In this study, invasive weed optimization software GNU octave, dimensions of concrete gravity dam Koyna located in India optimized stability constraints. For this purpose, a cross-section with a length unit consists of eight geometric parameters as input variables, and other geometric parameters were defined using these variables. The result showed that invasive weeds are well-optimized dimensions of the dam as the volume of concrete in the construction of the dam at the current level measures 3633 cubic meters that optimal dropped 3353 cubic meters, which is a mean of 7.7% of the value of the objective function (the volume of concrete in dams) is reduced. This amount of concrete decreased the construction of the dam, saving the cost and is more economical

A Multi-Objective HBMO-Based New FC-MCR Compensator for Damping of Power System Oscillations

In this paper, a novel compensator based on Magnetically Controlled Reactor with Fixed Capacitor banks (FC-MCR) is introduced and then power system stability in presence of this compensator is studied using an intelligent control method. The problem of robust FC-MCR-based damping controller design is formulated as a multi-objective optimization problem. The multi-objective problem is concocted to optimize a composite set of two eigenvalue-based objective functions comprising the desired damping factor, and the desired damping ratio of the lightly damped and undamped electromechanical modes. The controller is automatically tuned by optimization of an eigenvalue-based multi-objective function using Honey Bee Mating Optimization (HBMO) to simultaneously shift the lightly damped and undamped electromechanical modes to a prescribed zone in the s-plane so that the relative stability is guaranteed and the time domain specifications concurrently secured. The effectiveness of the proposed controller in damping low frequency oscillations under different operating conditions is demonstrated through eigenvalue analysis and nonlinear time simulation studies. The results show that the tuned HBMO-based FC-MCR controller which is designed by using the proposed multi-objective function has an outstanding capability in damping power system low frequency oscillations and significantly improves the power systems dynamic stability

Optimization of Reservoir Operation Using Cuckoo Search Algorithm: Example of Adiguzel Dam, Denizli, Turkey

The Adiguzel Dam is located in Denizli in the western part of Turkey. It was built for irrigation purposes, but it also produces energy at the same time. The dam's energy-production regime is not regular since there are no reservoir-operating rules. Thus, this study develops a reservoir optimization rule to generate a corresponding gain in energy production. It is well known that operating a reservoir is a complex problem that depends on many parameters such as inflow, storage capacity, water elevation, tailwater elevation, and evaporation. Therefore, in order to optimize energy production, there is a need to use heuristic algorithms such as the Cuckoo Search (CS). This study develops a CS algorithm-based solution to optimize the reservoir's operational system and generate an optimal operation rule curve. Results show that the CS algorithm improves the system operation, and the energy production will be increased by about 10% to a value of 160000 MWh with a corresponding economic gain of about $12 x 10(6) in total for 183 months