Sasan Harifi

Network reliability optimization is an optimization problem that focuses on finding an optimal solution for a reliable network design. In network reliability optimization, the goal is to maximize network reliability so that the overall cost of the network is reduced at the same time. The discussion of the reliability of various systems in the field of industry and engineering is of great importance, that's why reliability optimization has received a lot of attention in recent decades. Since this problem is included in the category of NP-Hard problems, the use of soft computing methods will be highly effective in solving it. In this paper, an approach based on the Giza Pyramids Construction (GPC) metaheuristic algorithm is proposed to solve the network reliability problem. For this purpose, two independent single objective functions with constraints are defined, and then the reliability of the network is calculated through optimistic estimation using the upper bound method. In order to compare the performance, 12 types of diverse and complex network models have been generated and the proposed algorithm has been compared with 10 popular and state-of-the-art algorithms. Statistical analysis has been used to find significant differences in the performance of algorithms. Also, a real model of the university network has been generated, investigated, and solved as a case study. The results of experiments, statistical analysis, and observations show that the proposed algorithm has a better performance than other metaheuristic algorithms and the proposed approach in solving reliability is an effective and low-cost approach.

Nature acts as a source of concepts, mechanisms, and principles for designing artificial computing systems to deal with complex computational problems. Most heuristic and metaheuristic algorithms are taken from the behavior of biological systems or physical systems in nature. Clustering is the process of grouping a set of data and putting it in a class of similar examples. Since the clustering problem is an NP-hard problem, using metaheuristics can be an appropriate tool to deal with these issues. Indeed, clustering is a special case of an optimization problem. In classic clustering, knowing the number of clusters is required before clustering. This paper presents an algorithm that requires no prior knowledge to classify the data. In this paper, we proposed a swarm-based Emperor Penguins Colony (EPC) algorithm to solve both classic and automatic clustering problems. The proposed approach is compared with six state-of-the-art, popular, and improved nature-inspired algorithms, a partitioning-based heuristic algorithm, and a hierarchical clustering method on ten real-world datasets. The results show that classic and automatic clustering using the EPC algorithm has better performance in comparison with other competing algorithms.

Genetic algorithm is an exploratory method inspired by Darwin's theory of natural evolution. This algorithm reflects the natural selection process in which suitable individuals are selected for reproduction to produce the offspring of the next generation. The genetic algorithm uses three main operators, namely selection, crossover, and mutation, each of which is involved in producing better strings or chromosomes. Among the three main operators, the mutation operator is one of the most important operators to achieve the optimal solution. The mutation operator is an intelligent mechanism for local search in the problem-solving search space. Mutations are therefore used to maintain population diversity and prevent premature convergence in the problem-solving process. In this study, to improve the genetic algorithm, a new type of mutation is introduced, which is called the Zigzag mutation. This mutation, by observance the zigzag pattern and making sudden and noticeable mutants in the gene compared to the existing mutations, make the local search in the problem space more efficient and helps to improve the genetic algorithm. In this paper, the proposed Zigzag mutation-based genetic algorithm is compared with six other genetic algorithms with different mutations in similar competitive conditions. The state-of-the-art mutations used in this study include Gaussian, Insertion, Inversion, Scramble, Swap, and Uniform, which are compared one by one with the proposed Zigzag mutation. In the experiments, 27 benchmark test functions are used to evaluate the performance. The evaluation results show superiority in 21 benchmark functions. According to the results, the presented method alone is better than other comparable methods in 77% of the benchmark functions. The share of the other six methods is only 23%. Also as an application, the presented improved genetic algorithm has been used in the segmentation of miscellaneous and aerial images. The results and statistical analysis show that the Zigzag mutation can improve the genetic algorithm and make it more efficient to solve the problems.

The knapsack problem is one of the combinational optimization issues. This problem is an NP-hard problem. Soft computing methods, including the use of metaheuristic algorithms, are one way to deal with these types of problems. The standard Giza Pyramids Construction (GPC) algorithm is the first ancient-inspired algorithm that is published recently. In this paper, a binary version of the GPC algorithm (BGPC) for solving the 0-1 knapsack problem is proposed. For this purpose, this study uses both accumulative and multiplicative penalty functions as the objective function to determine infeasible solutions. To compare the performance, thirty different datasets have been created and the proposed algorithm has been compared with four popular and state-of-the-art algorithms. Statistical analysis has been used to find a significant difference in the performance of algorithms. The results and statistical analysis show that the proposed algorithm performs better than other metaheuristic algorithms.

In recent years, the number and severity of natural disasters have increased. These disasters always carry the risks of mortality and injury. But its mortality risks are much higher for people with disabilities than for normal people. One of the most important issues during natural disasters is to pay attention to the provision of accommodation and the possibility of evacuating people with disabilities. In this paper, a Mixed-Integer Linear Programming (MILP) model is proposed for shelter locations, evacuation routing, compatibility constraints in the context of disability, and time windows to picking up people with disabilities from different locations and transporting them to shelters. This study considers the time required for the movement of people with disabilities, heterogeneous vehicles, different capacities, several depots, and several shelters. The intended objective functions are the total distance traveled by vehicles, the maximum distance of a tour, and a hybrid objective function created by the weighted combination of both mentioned objective functions. A new Giza Pyramids Construction (GPC) algorithm is used to solve the model. Using the approach of the GPC algorithm, which is one of the soft computing methods, greatly reduces the computation complexity. To examine the performance of the proposed algorithm, thirty instances at different scales are generated with and without time window constraints. For validation, the results obtained from the proposed algorithm are compared with the results obtained from four algorithms including SA, IWO, DE, and EPC. The results show that the GPC algorithm performed better than other algorithms in solving the model. According to the results, the proposed GPC algorithm is 5%, 44%, 10%, and 4% better than SA, IWO, DE, and EPC, respectively. Furthermore, considering the time window significantly increases the total network evacuation time. Based on our experiments the proposed model and solution approach can be helpful in solving the real problems related to the evacuation of people with disabilities during natural disasters.

Accumulative structure or cluster-like shape is one of the important features of social networks. These structures and clusters are communities in a complex network and are fully detectable. Common group behaviors of different communities can be categorized using community detection methods. Categorize behavior allows the study of each part of the network to be done centrally. This paper uses trust-based centrality to detect the communities that make up the network. Centrality determines the relative importance of a node in the graph of social networks. Redefining the trust-based centrality makes it possible to change the position in the analysis of centrality and separates the local central nodes and global central nodes. Then, a trust-based algorithm is proposed to express the strength of trust penetration conceptually between nodes to extract communities in networks. This method has led to the achievement of a flexible and effective community detection method. The proposed algorithm is applied to four benchmark networks. The experiments consist of two independent parts. The first part is to use the proposed algorithm to detect clusters and communities. After that, the algorithm is compared with a Girvan-Newman inspired method. The second part is the implementation of the proposed algorithm with a large number of iterations with the aim of modularity maximization and comparing it with other community detection algorithms. Although, the modularity criterion has been used to validate and compare the solution quality in both independent parts of the experiments. The results show about 1.4% to 5.2% improvement in community detection.

The idea of hybrid algorithms is formed due to the functional and structural differences in optimization algorithms. The goal is to create hybrid algorithms that can combine the strengths of the optimization algorithms to perform better in solving different problems. The Emperor Penguins Colony (EPC) algorithm is a population-based and nature-inspired optimization algorithm. This algorithm is powerful in finding global optima. In this paper, the standard EPC is improved by combining with genetic operators to finding better global optima. The genetic crossover and mutation operators have been used for modifying the decision vectors. These operators can cause a balance between exploration and exploitation. The balance between exploration and exploitation is effective in achieving a better optimal solution. The proposed algorithm called Hybrid-EPC is compared with GA, PSO, standard EPC, and Hybrid-PSO and tested on 20 various benchmark test functions. Also as an application, the proposed Hybrid-EPC algorithm is used for community detection in complex networks. For this purpose, the algorithm is tested on four social datasets and is compared with other community detection algorithms. The results show that this hybridization improves the standard EPC algorithm and has been successful in community detection.

Recently, the development of new metaheuristic algorithms has become very expansive. This expansion is especially evident in the category of nature-inspired algorithms. Nature is indeed the source of the solution in many problems, but the developed algorithms in this category used almost the same procedure for optimization. Before the development of nature-inspired algorithms, evolutionary-based algorithms were introduced. It seems that there is a need for some kind of change in this area. This change can be found in the new generation of algorithm development inspired by the ancient era. Ancient inspiration brings together all the positive aspects of nature and evolution. This paper discusses some applications of the ancient-inspired Giza Pyramids Construction (GPC) algorithm compared to the nature-inspired Emperor Penguins Colony (EPC) algorithm. Applications discussed in this paper include improving k-means clustering and optimizing the neuro-fuzzy system. Results from experiments show that the ancient-inspired GPC algorithm performed superior and more efficiently than algorithms inspired by other sources of inspiration.

Nowadays, many optimization issues around us cannot be solved by precise methods or that cannot be solved in a reasonable time. One way to solve such problems is to use metaheuristic algorithms. Metaheuristic algorithms try to find the best solution out of all possible solutions in the shortest time possible. Speed in convergence, accuracy, and problem-solving ability at high dimensions are characteristics of a good metaheuristic algorithm. This paper presents a new population-based metaheuristic algorithm inspired by a new source of inspiration. This algorithm is called Giza Pyramids Construction (GPC) inspired by the ancient past has the characteristics of a good metaheuristic algorithm to deal with many issues. The ancient-inspired is to observe and reflect on the legacy of the ancient past to understand the optimal methods, technologies, and strategies of that era. The proposed algorithm is controlled by the movements of the workers and pushing the stone blocks on the ramp. This algorithm is compared with five standard and popular metaheuristic algorithms. For this purpose, thirty different and diverse benchmark test functions are utilized. The proposed algorithm is also tested on high-dimensional benchmark test functions and is used as an application in image segmentation. The results show that the proposed algorithm is better than other metaheuristic algorithms and it is successful in solving high-dimensional problems, especially image segmentation.

In the present day markets, it is essential for organizations that manage their supply chain efficiency to sustain their market share and improve profitability. Optimized inventory control is an integral part of supply chain management. In inventory control problems, determining the ordering times and the order quantities of products are the two strategic decisions either to minimize total costs or to maximize total profits. This paper presents three models of inventory control problems. These three models are deterministic single-product, deterministic multi-product, and stochastic single-product. Due to the high computational complexity, the presented models are solved using the Emperor Penguins Colony (EPC) algorithm as a metaheuristic algorithm and a soft computing method. EPC is a newly published metaheuristic algorithm, which has not yet been employed to solve the inventory control problem. The results of applying the proposed algorithm on the models are compared with the results obtained by nine state-of-the-art and popular metaheuristic algorithms. To justify the proposed EPC, both cost and runtime criteria are considered. To find significant differences between the results obtained by algorithms, statistical analysis is used. The results show that the proposed algorithm for the presented models of inventory control has better solutions, lower cost, and less CPU consumption than other algorithms.

A neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system using approximate techniques of neural networks. Both neural network and fuzzy system have common features. These can solve problems that there is no mathematical model for them. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an adaptive network that uses supervised learning on learning algorithm. Selecting optimization method in training, to achieve effective results with ANFIS is very important. Heuristics and metaheuristics algorithms attempt to find the best solution out of all possible solutions of an optimization problem. ANFIS training can be based on non-derivative algorithms. Heuristics and metaheuristics are non-derivative algorithms that can lead to better performance in ANFIS training. Most heuristic and metaheuristic algorithms are taken from the behavior of biological systems or physical systems in nature. The newly released Emperor Penguins Colony (EPC) algorithm is a population-based and nature-inspired metaheuristic algorithm. This algorithm has many potential for solving various problems. In this paper an optimized ANFIS based on newly EPC algorithm is proposed. The optimized ANFIS is compared with other non-derivative algorithms on benchmark datasets. Eventually, the proposed algorithm is used to solve the classical inverted pendulum problem. The results show that the proposed ANFIS based on EPC algorithm has less error and better performance than other state-of-the-art algorithms in both training and testing phase.

A metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Metaheuristic algorithms attempt to find the best solution out of all possible solutions of an optimization problem. A very active area of research is the design of nature-inspired metaheuristics. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing systems to deal with complex computational problems. In this paper, a new metaheuristic algorithm, inspired by the behavior of emperor penguins which is called Emperor Penguins Colony (EPC), is proposed. This algorithm is controlled by the body heat radiation of the penguins and their spiral-like movement in their colony. The proposed algorithm is compared with eight developed metaheuristic algorithms. Ten benchmark test functions are applied to all algorithms. The results of the experiments to find the optimal result, show that the proposed algorithm is better than other metaheuristic algorithms.

Today, the world is moving towards becoming smart. Tools and equipment are somewhat smart and now it’s time to use them for making smart the organizations and special places. Integration of smart devices and using them in a special place makes it to be smart place. Human is always looking for the best decision as soon as possible in an emergency situation. This becomes more important when we speak about human lives. Having a smart system for hospitals can reduce the concerns. In this paper first the ideas to have a smart hospital are presented and then a Petri Net model is designed for it. The detailed rules for design of Petri Net model make it easy to transform the initial heuristic selection criteria in formalized procedures of model construction. The model proposed in this paper can be used in different levels with proper and purposefully development.

Clustering of big data has received much attention recently. Analytics algorithms on big datasets require tremendous computational capabilities. Apache Spark is a popular open- source platform for large-scale data processing that is well-suited for iterative machine learning tasks. This paper presents an overview of Apache Spark Machine Learning Library (Spark.MLlib) algorithms. The clustering methods consist of Gaussian Mixture Model (GMM), Power-Iteration Clustering method, Latent Dirichlet Allocation (LDA), and k-means are completely described. In this paper, three benchmark datasets include Forest Cover Type, KDD Cup 99 and Internet Advertisements used for experiments. The same algorithms that can be compared with each other, compared. For a better understanding of the results of the experiments, the algorithms are described with suitable tables and graphs.

For all countries an integrated and purposefully police system to investigate the incident occurred, has special importance. The existence of a system that can quickly make the best decision, is very important. Today, decision support systems are reached to an important and special position. Decision support systems can be combined with different issues to better deciding in special conditions. In this paper decision support for a police vehicle command and control system to response to incidents has been introduced. This system can make the best decision in allocate resources for incidents. The proposed system is modeled by using Petri Nets. The detailed rules for design of Petri Net model make it easy to transform the initial heuristic selection criteria in formalized procedures of model construction. The solution provides a dynamic complement to the static modeling and operational flexibility. The model proposed in this paper can be used in different levels with proper and purposefully development.

By increasing security requirements, biometric as a perfect solution for people identification is used. One of the biometrics which is considered as the most accurate and reliable method, is iris biometric. The iris biometric is about analysis of patterns in the iris texture. The operation can be done in several stages. Image acquisition, preprocessing and main processing including segmentation, normalization, feature extraction and matching are different stages of iris recognition. In this paper the previous works for different stages are proposed separately and classified, with the appropriate tables. Since there are many different methods for iris recognition, this survey covers some of the significant methods.

Iris segmentation plays a very vital role for iris recognition. In the meantime, high quality image with many details has important role in segmentation. Using High Dynamic Range technique, image details can be increased. In this paper, using converting iris images into HDR iris images, segmentation operation is performed. Images are changed to different exposure manually, then are combined and produced HDR image. HDR images and normal images are tested and compared separately with available segmentation methods. Experiment results show that, the proposed method can segment iris images accurately and can be used for different types of iris images.

In this letter, for the first time, a novel source of inspiration for the development of metaheuristic algorithms is introduced. This source of inspiration, which is called the ancient-inspired, by combining all good features of the current source of inspirations can lead to the development of efficient algorithms. There have been numerous limitations in the ancient era, but various man-made structures indicate that limitations and lack of facilities have led to some sort of optimization. Technological breakthroughs and specific strategies in the ancient era have created ancient relics and structures some of which have remained to this day. A closer look at these ancient relics shows that the methods, strategies, and technologies used in antiquity are far more advanced and optimized than we would have imagined. While introducing this novel source of inspiration, this letter offers researchers a new classification of metaheuristic algorithms.