Various methods have Been proposed to determine the ultimate pit limits. Artificial intelligence-based methods such as heuristic genetic algorithms, ants and Colony competition algorithms. Artificial Bee Colony (ABC) algorithm is one of the most powerful heuristic algorithms inspired by the social life of Bees. In this paper, a hypothetical example of the life of Bees is primarily described to find the source with the highest amount of nectar by this algorithm. Then, a method based on the Bee algorithm is proposed to determine the ultimate pit limit, and to examine its performance, a two-dimensional example is described step by step. In this example, it was found that in cases where the moving cone can not find the optimal ultimate pit limit, the ABC method is well suited for introducing solution. Then, this algorithm was used to determine the ultimate limits of Sungun Copper mine pit with a number of 120*100*45 blocks. To validate the proposed algorithm, the graph theory and moving cone techniques were used. Results showed that the profit obtained by ABC algorithm is just 1. 6% less than that of graph theory algorithm, which is a rigorous technique and entails finding the true optimum. Meanwhile, the ABC algorithm provides 12. 3% more profit when compared to heuristic moving cone algorithm.

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.

Acridoidea Stirred Artificial Bee Colony (ASA) Algorithm is applied to solve the power loss reduction problem. In the projected algorithm natural Acridoidea jumping phenomenon has Been imitated and the modeled design has Been intermingled with Artificial Bee Colony Algorithm. In the proposed algorithm position update has Been done through the distance of jumping done by Acridoidea. The distance (D) is horizontal (h) with angle (θ ), velocity (V) parameter amplifying the rate which based on the gravity of Ballistic projectile. Normally the angle will be 45◦ horizontal and it depends on the take-off velocity. ASA Algorithm has Been tested in standard IEEE 57 bus test system and results show that the proposed ASA algorithm reduced the real power loss effectively.

Artificial Bee Colony (ABC) is one of the recently introduced optimization methods based on intelligent behavior of honey Bees. In this work, we propose an Adaptive Multi-Objective Artificial Bee Colony (A-MOABC) Optimizer which uses Pareto dominance notion and takes advantage of crowding distance and windowing mechanisms. The employed Bees use an adaptive windowing mechanism to select their own leaders and alter their positions. Besides, onlookers update their positions using food sources presented by employed Bees. Pareto dominance notion is used to show the quality of the food sources. Those employed or onlooker Bees which find food sources with poor quality turn into scout Bees in order to search other areas. The suggested method uses crowding distance technique in conjunction with the windowing mechanism in order to keep diversity in the external archive. The experimental results indicate that the proposed approach is not only thoroughly competitive compared to other algorithms considered in this work, but also finds the result with satisfactory precision.

Mobile ad hoc networks (MANETs) are multi-hop wireless networks of mobile nodes constructed dynamically without the use of any fixed network infrastructure. Due to inherent characteristics of these networks, malicious nodes can easily disrupt the routing process. A traditional approach to detect such malicious network activities is to build a pro le of the normal network traffic, and then identify an activity as suspicious if it deviates from this profile.As the topology of a MANET constantly changes over time, the simple use of a static pro le is not efficient. In this paper, we present a dynamic hybrid approach based on the Artificial Bee Colony (ABC) and negative selection (NS) algorithms, called BeeID, for intrusion detection in AODV-based MANETs.The approach consists of three phases: training, detection, and updating. In the training phase, a niching Artificial Bee Colony algorithm, called NicheNABC, runs a negative selection algorithm multiple times to generate a set of mature negative detectors to cover the nonself space. In the detection phase, mature negative detectors are used to discriminate between normal and malicious network activities. In the updating phase, the set of mature negative detectors is updated by one of two methods of partial updating or total updating. We use the Monte Carlo integration to estimate the amount of the nonself space covered by negative detectors and to determine when the total updating should be done. We demonstrate the effectiveness of BeeID for detecting several types of routing attacks on AODV-based MANETs simulated using the NS2 simulator. The experimental results show that BeeID can achieve a better tradeoff between detection rate and false-alarm rate as compared to other dynamic approaches previously reported in the literature.