In recent years, damage identification of structures becomes more attractive for researchers in order to quantify the condition of structural system during service life. Moreover, identifying the damage location and severity is very important after disasters such as earthquake and terrorist attak. Structures can also be damaged by normal activity such as corrosion, aging, fatique, wind, waveload, etc. Therefore, the structural health monitoring is an emerging field to ensure good performance of structures. In this paper, identification of the location and severity of damages in structures are studied by analytical method using ARTIFICIAL BEE COLONY OPTIMIZATION (ABC). In the analytical method, the mass and stiffness matrices of structure can be determined by the finite element procedure. Considering the stiffness matrix of healthy structure and that of the damage structure, the location and severity of the damage can be determined. It is assumed that the global mass matrix remains unchanged after the damage occurs in the structure. The natural frequencies and mode shapes of damaged structure can be obtained by measurement. In the study, the damage characteristics are known. Then by applying the eigenvalue equation, the stiffness matrix is determined for damaged structure. Finding the location of damage is introduced as an inverse problem. The conventional methods are very expensive and time consuming, while meta-heuristic methods are capable to solve complex OPTIMIZATION problems. Swarm intelligence algorithm introduces the collective behavior of social insects colonies to solve OPTIMIZATION problems. ARTIFICIAL BEE COLONY algorithm is an evolutionary computing method, which was developed, based on the intelligent foraging behavior of honeyBEE swarm. Each food source is considered as a possible solution. The location and quality of the nectar from the flower is related to the damage properties and fitness function, respectively. The dimension of every ARTIFICIAL employed BEE is equal to the number of member of the structure. Then quality value of the food source is evaluated by the fitness function. The best fitness value is memorized in each search. When the fitness value is improved after a predefined iteration, the new possible solution will be considered. In the ABC process, the number of food source, the limit and the maximum cycle number are three control parameters. In the OPTIMIZATION problem, applying a proper objective function is one of the indispensable part of the process. Since the structural damage detection is a highly nonlinear problem, a proper objective function can detect the damage accurately and quickly. There are various methods for damage detection, which generally can be classified into two categories, static and dynamic method. Because of the efficiency of the dynamic method, the objective function is selected based on the dynamic technique, which utilizes the eigenvalue problem. In the mathematical equation of the objective function, the mass and stiffness matrix of healthy structure is defined by finite element method. The natural frequencies and mode shapes obtained by the measurement or modeling the structure. The stiffness matrix of damaged structure is determined with the OPTIMIZATION algorithm to minimize the objective function. In a measurement test, the used sensors cannot detect all of the degrees freedom of a structure, therefore the obtained information in measurement include a limited number of frequencies or mode shapes. In addition, to avoid a time consuming process, it may be decided to utilize only a limit number of frequencies obtained by the measurement. The system equivalent reduction expansion process (SEREP), which is an accurate and efficient technique of model reduction, is utilized in the paper. Moreover, the damage detection is examined through three numerical examples, plane and space truss and palne frame, each one has two damage scenarios, which include noisy measurement data. The results indicate that the proposed method is a powerfull procedure to detect damages in structures.

Renewable energy sources are developed worldwide, owing to high oil prices and in order to limit greenhouse gas emissions. The objective of this research was to study the feasibility of biodiesel production from mountain almond (Prunus Scoparia) oil using ultrasonic system and OPTIMIZATION of the process using ARTIFICIAL BEEs COLONY (ABC) Algorithm. The results showed that by increasing the molar ratio, the conversion percentage increased and after reaching a certain ratio, further increase in the molar ratio caused decrease in the conversion percentage. Increasing in the ultrasound amplitude resulted in an increase in the conversion percentage which tends to ascend; Furthermore, results of OPTIMIZATION showed that the amount of molar ratio, amplitude, pulse and reaction time were 5.6, 0.90, 0.33 and 5 min, respectively. For independent variables, the values of yield and energy consumption were obtained which were equal to 96.1% and 9912 J, respectively. This finding proves that ABC algorithm can estimate the optimum point in biodiesel production with high accuracy.

Flood routing, as a mathematical method for predicting the changing magnitude and celerity of a flood wave as it propagates down rivers, provides the substantive bases for conducting flood zonation, flood forecasting and design of river structures. The Muskingum technique is one of important and most frequently used techniques of flood routing. In the present research, accuracy of some methods for OPTIMIZATION of Muskingum parameters (K, x and m) including graphical technique, linear programming, genetic algorithm, ant COLONY algorithm and ARTIFICIAL BEE COLONY algorithm has BEEn evaluated by using statistics of 9 flood hydrographs contemporaneous detected in two hydrometric stations of Mola Sani and Ahwaz in a 63-km reach of Karun River called Mola Sani-Ahwaz. In order to evaluate the different techniques, statistical criteria of the root-mean-square error (RMSE), relative error (RE), mean absolute error (MBE), mean error deviation (MAE), Nash and Sutclife coefficient (NS), coefficient of determination (R2) and also visual comparison by plotting estimated and observed hydrographs were used. The results showed that ARTIFICIAL BEE COLONY and genetic algorithms with RMSE of 79.85 m 3/s were found to be superior to graphical technique with RMSE of 88.07 m3/s for estimation of Muskingum model parameters. Comparing peak flow of hydrographs indicate more accurate for graphical technique with mean absolute error (MAE) of 31.2 m3/s than genetic algorithm with MAE of 58.8 m3/s and ARTIFICIAL BEE COLONY algorithm with MAE of 62.18 m3/s.

Unit commitment is one of the main problems in power systems operation and since there are plenty of constraints and parameters it is too complicated. In this paper, a new method based on ARTIFICIAL BEE COLONY algorithm has BEEn proposed to solve the problem of unit commitment. In the proposed method, a novel coding approach is presented which uses integer numbers (for satisfying minimum up/down time constraints) and binary numbers (for satisfying spinning reserve constraint). One of the advantages of the proposed coding approach is elimination of using penalty factors in the OPTIMIZATION process to constraints handling. The total cost of unit commitment can be truly minimized using the proposed algorithm. In comparison with other existing methods in this regard, the simulated results and numerical studies show better convergence of the proposed algorithm.

Eddy current testing is one of nondestructive testing (NDT). This test is used to investigate heat exchanger tubes and detecting any possible faults in these tubes. This paper aims to determine the optimum parameters of probe in order to improve sensitivity and inspection system performance. Firstly, this paper presents equations related to design and characteristics of probe to investigate sample tubes. Then, optimum design is presented in order to reach the highest signal to noise ratio (SNR) and sensitivity (S) using ARTIFICIAL BEE COLONY (ABC). Finally, eddy current testing is performed for optimum probe using finite element analysis (FEA).

OPTIMIZATION methods are one of the strongest tools for managing time and decreasing unnecessary costs in operational issues. The purpose of OPTIMIZATION, regarding constraints and requirements, is to find an appropriate and acceptable solution to a problem. Since; most of the combinatorial OPTIMIZATION problems, such as Travelling Salesman Problem (TSP) and different types of Vehicle Routing Problem (VRP), are subcategories of NP-Hard class, expert recommendations are toward solving these kinds of problems by metaheuristic algorithms such as ARTIFICIAL BEE COLONY (ABC) algorithm instead of exact solving methodologies.In this paper, a comprehensive study was conducted on the background of ARTIFICIAL BEE COLONY algorithms and the results of its application on various transportation problems. The formulation of Vehicle Routing Problem and its constraints was also discussed. The results show that the ABC algorithm has a significant power to improve solving various problems. As an intuitive summary, one can refer to Szeto et al. (2010) who proposed an ABC algorithm for solving the Capacitated Vehicle Routing Problem in which the mean percentage improvement of the average results of all test instances was 4.16% and the best percentage improvement was 3.53%. Further, a Hybrid ABC algorithm was designed by Zhang et al. (2014) for one of the latest Vehicle Routing Problems. They implemented the algorithm for Environmental Vehicle Routing Problem which outperforms the original ABC algorithm by 5% on average. Therefore, it can be concluded that the ARTIFICIAL BEE COLONY algorithm is very successful in improving the results of this kind of experiment.In completion of the above-mentioned, the results of the proposed ABC algorithm by this study for solving Travelling Salesman Problem and Vehicle Routing Problem with Simultaneous Pickup and Delivery confirmed the expressed idea. As a result, the assumed algorithm improved instances of TSP about 1.03% and 8.88% which were named gr120 and gr202, respectively. It also enhanced the CMT1X and CMT3X instances in VRP-SPD about 0.41% and 1.31%, respectively. This certificates the quality, high capacity and preference of the ABC algorithm.

Nowadays due to development of distribution systems and increase in electricity demand, the use of distributed generation (DG) sources and network reconfiguration in parallel are increased. This paper proposes a new multi-objective OPTIMIZATION to optimize network topology and allocation of DG in distribution network. In the proposed algorithm, ant COLONY OPTIMIZATION provides the network configuration and the ARTIFICIAL BEE COLONY algorithm optimizes the location and size of DGs. An external repository is considered to save non-dominated (Pareto) solutions found during the search process. Moreover, a fuzzy-based decision maker is adopted to select the ‘best’ compromised solution among the non-dominated optimal solutions of multi-objective OPTIMIZATION problem. The objectives consist of minimize network power losses, better voltage regulation and improve the voltage stability within the frame-work of system operation. The proposed algorithm is implemented on the IEEE 33-bus distribution test system. Furthermore, the simulation results demonstrate the effectiveness of the proposed algorithm compared to those of other methods.

Intrusion in the network is increasing. Intrusion detection system can greatly prevent network attacks. Feature selection is a critical issue in intrusion detection systems which have a considerable impact on the accuracy and effectiveness of the system. In this study, a new hybrid network intrusion detection system with improved ARTIFICIAL BEE COLONY algorithm using support vector machine classifier is proposed for feature selection. The main idea is utilizing a combination of search equations of particle swarm OPTIMIZATION and Differential Evolution for updating BEE’ s position of employed and onlooker BEEs and utilizing levy flight on scout BEEs phase, to improve exploitation and increase the convergence rate of the standard ARTIFICIAL BEE COLONY algorithm. The robustness and stability of the proposed approach is evaluated on NSL-KDD dataset and showed significant improvement on the overall performance of intrusion detection system with an accuracy of 98. 97 percent.