Chemical Based ABC **Swarm** **Intelligence** **Algorithm**: With a little modification in ABC, the above problems in the **Swarm** system invivo can be solved. In the task allocating system ABC, pheromone trail concept of ACO has been combined to create definite traffic routes. This traffic routing ensures that blood vessels are not blocked during the **swarm** movement and at no point of time their concentration in blood plasma crosses a certain value that can affect the viscosity of blood with respect to the vessel walls. Further, this guarantees the return of any lost nanorobot to the **swarm** because of its affinity for the pheromone trail on its own accord. Nanorobots obtain their sense of direction by two switching onboard chemo-tactic sensors (Tag Hogg et al., 2006), one for E-Cadherin and other for pheromone molecules. Nanorobots can be divided into two types: Lookout and Worker Nanorobots. Both differ in their designs according to the need. While Worker Nanorobots (WN) have both target specific and attractant or pheromone specific sensors active simultaneously, in Lookout Nanorobots (LN) functionality of these two sensors switch if their specific threshold values are met. Pheromone or attractant molecule define traffic routes to the target site, (Sharma et al., 2014). It can be a time consuming approach, but can guarantee complete exploration and optimal results. Major steps in this **algorithm** are:

Program designed for each **algorithm** is made up of the same number of agents, decision variables, number of iterations and upper and lower bounds to compose the search space, a mathematical function to be optimized, and an optimizer, which is the meta heuristic technique used to perform the optimization process. Furthermore, they are bundled to an Opytimizer class, which holds all the vital information about the optimization task. Finally the task is started, and when it finishes, it returns a history object which encodes valuable data which include iteration count, fitness, position and time taken to complete the experiment about the optimization procedure. The experiment is repeated twenty times with twenty iterations. The input variables and the number of iterations remain the same for all the algorithms in other to easily compare and obtain a better result.

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Secondly, as the computing consumption of particle filtering is already very large, so the computing con- sumption of new introduced PSO process should be re- duced. In the PSO-PF **algorithm**, for every particle, at first, a random number in the range of 0 and 1 will be generated, and only when the number is smaller than a predefined threshold, the PSO process can be conducted. A new type of PSO process — one step predefined PSO is introduced. In this process, only when the new location is better than the original one, the particle will move to the new one, and the location updating process will be conducted only one time in each particle filtering genera- tion.

Abstract-- Intrusion detection system (IDS) is a mechanism used for the detection of malicious attack. In this paper intrusion detection with different combination of neural network are used to achieve a good accuracy. We use five different set of data sets namely DEFCON, NSL-KDD, DARPA, ISCX-UNB and KDD 1999 Cup. In existing systems there is no pre-processing and optimization. Without pre-processing the redundant data cannot be removed so we proposed a new **swarm** **intelligence** approach to pre-process the data. It converts the non-numerical value into numerical value. Also it remove the noise and irrelevant data. Pre-processed data is trained by five different types of Neural Networks they are Feed Forward Neural Network (FFNN), Deep Neural Network (DNN), and Joint Evolution Neural Network (JENN) using Genetic **Algorithm**, Radial Basic Function Neural Network (RBNN) and Hybrid Neural Network (HNN).After implementing these network function an Artificial Bee Colony optimized method is applied to give a better accuracy rate and efficiency to enhance the system.

In conclusion, the objective of the research was to conduct an experiment using Opytimizer to determine/measure the performance (solution quality and time complexity) of three nature-inspired algorithms; particle **swarm** optimization, bat **algorithm** and artificial bee colony to determine which of the algorithms converge faster. Opytimizer python micro-framework was used on several benchmark functions. The experiment was run several times and the mean, best and worst cases were recorded. It revealed that PSO converge to global optimum faster than both the other two; BA and ABC. In terms of quality of solution ABC outperformed the rest of the algorithms. We have used basic versions of these algorithms without finely tuning the parameters to compare the results.

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Abstract: Technology has shrunk the global markets and information is accessible very quickly and effortlessly. Business organizations world over concentrate on their production systems to improve the quality of the end product, well distribute the product and optimize cost of resources. Transportation cost, inventory carrying cost and shortage costs constitute the major costs in cost of distribution. A competent supply chain always strives to manufacture the right quantity of end products and hold a minimum inventory across the entire supply chain. In thecurrent paper, a five echelon supply chain model is developed and it is optimized using particle **swarm** **intelligence** **algorithm**.

An Effective Hybrid Firefly **Algorithm** with Harmony Search for Global Numerical Optimization[3] expressed that the hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly **algorithm** (FA), namely, HS/FA, to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. These metaheuristic approaches are solving complicated problems, like permutation flow shop scheduling reliability, high-dimensional function optimization and other engineering problems. Have newly introduced on genetic algorithms (GA). In HS/FA, top fireflies scheme is introduced to reduce running time.

As a **swarm** **intelligence** optimization **algorithm**, in the PMA, the optimization variable x refers to living places, the objective function f (x ) refers to attractive of the residence place, the optimal solution (local optimal solution) refers to the most attractive place (beneficial region), the "up" or "mountain climbing" of **algorithm** refers to move to beneficial region, escaping from the local optimal refers to move out of beneficial region as a result population pressure; population flow corresponds to random, local searching method; and population migration corresponds to the way to choose the approximate solution like as population struggles upwards; population proliferation combines the overall searching with escaping from the local optimal strategy.

Figure 1 represents a flowchart for simple Artificial Bee Colony (ABC) **algorithm**. According to this **algorithm** an initial path is created which goes directly from the starting point to the target and turns if an obstacle is encountered. The length of the path is calculated in the next step [10]. The employed bees are then sent to the neighbourhood of breakpoints and the fitness of the new paths is calculated. In the next step the onlooker bees are sent to the selected breakpoint of the neighbourhood for exploitation. The fitness function is then calculated for new paths. In the next step the scout bees are sent for exploration and for finding new paths. The above steps are repeated until the most optimum path is found.

A bee colony is another example of **swarm** **intelligence** [13]. The bee nest is an initial state of bee colony transitions. Any bee colony exploits a mechanism called waggle dance to op- timize the food transporting to the nest. This mechanism is as follows. In the nest there is an area for communication among bees. At this area the bees knowing, where the food source is precisely, exchange the information about the di- rection, distance, and amount of nectar on the related food source by a waggle dance. The direction of waggle dancing bees shows the direction of the food source in relation to the Sun, the intensity of the waggles is associated to the dis- tance, and the duration of the dance shows the amount of nectar. Due to this form of communication the bee colony transitions are built up by the following steps [12]:

In order to design the optimal channel cross section, the **Swarm** **Intelligence** (SI) algorithms [7] such as Artificial Bee Colony **Algorithm**, Genetic **Algorithm** have been used to optimize problems across channel cross section over the last decades [8] . It is more efficient and accurate as compared to conventional methods for solving the highly nonlinear optimization problems. For example, Adaptive Particle **Swarm** Optimization (APSO) has successfully been used in continual cross sections optimization of the trapezoidal channels under the global condition. Liu et al. [9] used Cat **Swarm** Optimization (CSO) for obtaining optimal channel dimensions based on safety and stability of the side walls and found it was better in channel cross section under problem constraints its computational speed, and the robustness was not efficient.

Although chaotic search can avoid being caught in local minimum because of its ergodicity, pure chaotic search can obtain good solution only through huge iteration step numbers and it is sensitive to initial solution in particular. Therefore, a two-stage chaotic CS **algorithm** is put forward by combining CS **algorithm** with the above chaotic search, in which CS **algorithm** is used to lead global search and CO leads local search according to the result of CS **algorithm**. In order to maintain population diversity and strengthen the dispersion of the search, the **algorithm** keeps some superior individuals, dynamically contracts search range in view of the best position of the population, and replaces the worse nest position with the one generated in the contract region randomly. The steps of chaotic cuckoo search **algorithm** can be described as follows,

**Swarm** **intelligence** (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

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In Wireless Sensor Networks (WSN), sensors are randomly deployed in the sensor field which brings the coverage problem. It is a unique problem and in maximizing coverage, the sensors need to be placed in a position such that the sensing capability of the network is fully utilized to ensure high quality of service. This can be achieved with minimum number of sensor nodes having maximum coverage in the network and the nodes are within the communication range. In this paper, particle **swarm** **algorithm** was used to find the optimal positions of the sensors to determine the best coverage. This **algorithm** is an optimization technique which belongs to the fertile paradigm of **swarm** **intelligence**. It is a derivative free and is a very efficient global search **algorithm** with few **algorithm** parameters. Here, results are presented which shows that, PSO has good effect in solving coverage problem.

ABSTRACT: Big data is the data sets whose size is vast when compared to the capability of a advanced guard and assumption to imprison, orgnize, and procedure these data about data an endurable lapsed moment. The structural and unstructured records are the fast growth in ontology based challenges in performance more scalability and efficiently. The ant-colony **algorithm** is a probabilistic technique for solving large data sets problems which can be reduced time to find particular data sets. The issues in a traditional centralized reasoning methods is using in small datasets (TIF/EAT) the performance is less. In this paper new **algorithm** i.e Ant Colony **Swarm** **Intelligence** Optimization (ACOSIO) has been proposed for optimizing the unstructured big data to structured data. The proposed **algorithm** gives better performance in terms of scalability, less time and efficiency compared to Traditional centralized reasoning methods.

The robustness of PSO results from its use of **swarm** **intelligence** to search for the best solution to a complex problem. The **swarm** **intelligence** can be described as a system that automatically evolves by simulates the social behaviour of organisms, e.g., the social behaviour of knowledge sharing. By sharing valuable information, the behaviours of individuals in a **swarm** are optimized to achieve a certain objective. In PSO, an individual is considered a particle, which is a vector in the problem space. The information for the particle includes knowledge gained from its previous experience and knowledge gained from the **swarm**. The value of the particle, which is estimated by the objective function, is used to update its information and to optimize the objective of the **swarm**. Therefore, the **swarm** can converge to develop good resolution in local regions of the problem space; the common objective can also be updated when one particle finds a better objective so that the particle can lead the **swarm** in exploring a different region of the problem space. These superior search characteristics have made PSO the most popular evolutionary **algorithm** in several fields.

In this section, the implemented PSO **algorithm** has been outlined based on the fundamental concepts described above. The essential steps of this **algorithm** are represented in a flowchart diagram shown in Fig. 3. These steps describe that this **algorithm** is an iterative technique that searches the space to determine the optimal solution for an objective function (fitness function). The PSO **algorithm** evaluates itself based on the movement of each particle as well as the **swarm** collaboration. Each particle starts to move randomly based on its own best knowledge and the swarm’s experience. It is also attracted toward the location of the current global best position Xgbest and its own best position Xpbest. Therefore, the basic rules of this **algorithm** can be explained in three main stages [9]:

In PSO, when a particle discovers a good solution, other particles gather around the solution (gbest) too. There- fore they cannot escape from a local optimal solution. Consequently PSO cannot achieve global searches. In [12] the authors proposed a hybrid PSO/ACO Algo- rithm for discovering classification rules in Data Min- ing. The **algorithm** first generates a nominal rule and then adds continuous attributes with that rule in sequen- tial order. The sequential approach takes more time and there is no interaction between rules. The proposed pro- vide a solution for the above problem. It integrates Tabu search in PSO to explore the search space efficiently, and use cooperative coevolution computational model to create the interaction between swarms. It uses mul- tiple swarms (or sub-swarms) for searching a solution. The swarms exchange information after some iteration. The proposed **algorithm** use parallel cooperative coevo- lution computational model.

Low or unexpected rise in temperature is a major factor affect the effectiveness and productivity of broiler chickens. Maintaining and keeping the temperature at normal level is essential to reducing the mortality rate and increase the productivity of the poultry. Some Nature Inspired Algorithms (NIAs) which have proven to be efficient have been adopted to regulate the temperature of the poultry house. However, various studies have shown that there is no **algorithm** that can achieve the best solution for all optimization problems, and that some algorithms give a better solution for some problems than the others. Therefore, in this study, a comparative analysis of the Particle **Swarm** Optimization (PSO) and Gravitational Search **Algorithm** (GSA) in Poultry House Temperature Control System. The experiment results show that both PSO and GSA were able to regulated the poultry house efficiently. However, PSO proved to be more efficient than GSA in terms of cost and computational time. The PSO is able to find better solutions and converges faster compared to the Gravitational Search **Algorithm**. It is therefore, recommended that PSO should be adopted instead of GSA in poultry house temperature regulation systems.

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Abstract: Automated Classification of signals for medical diagnosis is a key research area in signal processing application domains. This paper deals with the classification of electroencephalogram (EEG) signal for the detection of epileptic seizures. The EEG signal is decom posed using discrete wavelet transform (DWT) for feature representation which is used as input to a classifier. The classifier is an artificial neural network (ANN) trained with population based meta-heuristic algorithms such as particle **swarm** optimization(PSO), gravitational search **algorithm** (GSA) and hybrid PSO and GSA. The algorithms are tested on a publicly EEG signal database and a comparative analysis is provided. The results showed that the ANN trained with hybrid PSO and GSA i.e. ANNPSOGSA performs better in terms of classification accuracy and mean square error (MSE).