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The Swarm Intelligence is a new and modern method employed in optimization problems. The Swarm Intelligence method is based on the en masse movement of living animals like birds, fishes, ants and other social animals. Migration, seeking for food and fighting with enemies are social behaviors of animals. Optimization principle is seen in these animals. The Artificial Fish Swarm Optimization (AFSA) method is one of the Swarm Intelligence approaches that works based on the population and stochastic search. Fishes show very intelligently social behaviors. This algorithm is one of the best approaches of the Swarm Intelligence method with considerable advantages like high convergence speed, flexibility, error tolerance and high accuracy. this paper review the AFSA algorithm, its evolution stages from the start point up to now, improvements and applications in various fields like optimization, control, image processing, data mining, improving neural networks, networks, scheduling, and signal processing and so on. Also, various methods combining the AFSA with other optimization methods like PSO, Fuzzy Logic, Cellular Learning Automata or intelligent search methods like Tabu search, Simulated Annealing, Chaos Search and etc.

A new approach for feature selection is presented in this paper. The proposed approach uses the Harmony Search with a novel fitness function to eliminate noisy and irrelevant features. Harmony vectors contain real weights which refer to feature space. The best and significant features are selected according to a threshold. Fitness function of Harmony Search is based on the Area Under the receiver operating characteristics Curve (AUC). All of the selected features are employed to improve the classification of the k Nearest Neighbor (k-NN) classifier. Experimental results claim that the proposed method is able to improve the classification performance of k-NN algorithm in comparison with the other important methods in realm of feature selection such as BAHSIC, FSS, BSS and MFS. Refereed/Peer-reviewed

The CPU scheduling is one of the most important tasks of the operating system. Many algorithms are designed and used in this regard each having advantages and disadvantages. In this paper a new algorithm for the CPU scheduling is presented using FFGA (Fonseca and Fleming’s Genetic Algorithm) multiobjective optimization. Contrary to the classical algorithms in use, it uses the three parameters of CPU burst time; I/O devices service time, and priority of process instead of using one parameter of CPU burst time. The important point is the adaptation of the algorithm which selects a special process depending on the system situation. The performance of this algorithm was compared with seven classical schedulingalgorithms (FCFS, RR (equal, prioritized), SJF (preemptive, non-preemptive, Priority (preemptive, nonpreemptive)), and the results showed that the performance of the proposed method is more optimized than other methods. The proposed algorithm optimizes the average waiting time and response time for the processes. Refereed/Peer-reviewed

The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (D max), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of D max and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of D max and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system's results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.

A new method of feature selection is presented in this paper. The proposed idea uses Particle Swarm Optimization (PSO) with fitness function in order to assign higher weights to informative features while noisy irrelevant features are given low weights. The measure of Area Under the receiver operating characteristics Curve (AUC) is used as the fitness function of the particles. Experimental results claim that the PSO-based feature weighting can improve the classification performance of the k-NN algorithm in comparison with the other important method in realm of feature weighting such as Mutual Information, Genetic Algorithm, Tabu Search and chi-squared (χ2). Additionally, on synthetic data sets, this method is able to allocate very low weight to the noisy irrelevant features which may be considered as the eliminated features from the data set.

The aim of this study is to design a Fuzzy Expert System to determine the concrete mix design. In the civil engineering, the determination of concrete mix design is so difficult and usually results in imprecision. Fuzzy logic is a way to represent a sort of uncertainty which is understandable for human. So, we can use the fuzzy logic to easily determine the concrete mix designs in a descriptive form. The input fields of system are Slump, Maximum Size of Aggregate (D max ), Concrete Compressive Strength (CCS) and Fineness Modulus (FM). The output fields are quantities of water, Cement, Fine Aggregate (F.A) and Course Aggregate (C.A). The experimental results show that the average error of predicted compressive strength for FIS is 6.43%, the minimum error of which is 4.73%. Refereed/Peer-reviewed

The aim of this study is to design a Fuzzy Expert System for heart disease diagnosis. The designed system based on the V.A. Medical Center, Long Beach and Cleveland Clinic Foundation data base. The system has 13 input fields and one output field. Input fields are chest pain type, blood pressure, cholesterol, resting blood sugar, maximum heart rate, resting electrocardiography (ECG), exercise, old peak (ST depression induced by exercise relative to rest), thallium scan, sex and age. The output field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4 (distinguish presence (values 1, 2, 3, 4)). This system uses Mamdani inference method. The results obtained from designed system are compared with the data in upon database and observed results of designed system are correct in 94%. The system designed in Matlab software. The system can be viewed as an alternative for existing methods to distinguish of heart disease presence. Refereed/Peer-reviewed