Recent interest has been generated in multimodal biometrics technology due to its potential to increase recognition rates by overcoming some of the fundamental limitations of single biometric modalities. A typical biometric recognition system will consist of components for sensing, feature extraction, and matching. The system’s robustness is dependent on the accuracy with which relevant data can be gathered from certain biometric features. This study presents a novel face-iris trait feature extraction approach for use in multimodal biometric systems. Among the state-of-the-art algorithms for biometric based iris/face recognition, image processing algorithms with deep learning will have an edge as they are very solid. The iris authentication fits the complex mathematical patterns of the irises which are drastically particular for each. A comprehensive look on biometric authentication located that the fake rejection price of iris authentication is only 1.8% which is the bottom and highest accuracy of 93.78%.

This paper presents a systematic approach for obtaining the best Hamiltonian circuit among specified nodes within a given superimposed graph (SG). To address this challenge, we introduce a formalism, Constraint Satisfaction Optimization Problem (CSOP), that finds the minimal Hamiltonian circuit in superimposed graphs. As a resolution method, we used updated versions of the genetic algorithm (GA), the improved GA, and the guided improved GA. After comparison, we find that our algorithm IG2A, combined with guided mutation strategies and improved with Dijkstra’s algorithm, optimizes the circuit-finding process more efficiently. Our contribution lies in developing a new Guided Genetic Algorithm (GGA). The well-known guided mutation strategies and efficient search heuristics enhance the latter. Our approach significantly outperforms the standard genetic algorithm in solving the HCP in superimposed graphs, providing better optimization results and faster convergence.

Graph theory has been since long time very helpful either in science or in real life. Since it has been introduced, it was considered like a crossroad in science. Many problems are computer science, in logistics, in physics, in electrical engineering, in automatic science were all inspired by graph theory. Moreover, sone algorithms such as the shortest path are omnipresent within our life. That is why nobody could ignore the importance of graph theory. Nowadays, the graph theory, as is, could not answer correctly to our new needs. In my point, for the current graph theory to answer much better to our new problem-solving requirements, the theory must include two more main pillars: the semantic factor and the superimposition factor. In this paper we introduce the all-new graph theory: the semantic superposed graph theory.

In the last few years, there has been an increasing interest in Random Constraint Satisfaction Problems (CSP) from both experimental and theoretical points of view. To consider a variant instance of the problems, we used a random benchmark. In the present paper, some work has been done to find the shortest Hamiltonian circuit among specified nodes in each superimposed graph (SGs). The Hamiltonian circuit is a circuit that visits each node in the graph exactly once. The Hamiltonian path may be constructed and adjusted according to specific constraints such as time limits. A new constraint satisfaction optimization problem model for the circuit Hamiltonian circuit problem in a superimposed graph has been presented. To solve this issue, we propose amelioration for the genetic algorithm using Dijkstra’s algorithm, where we create the improved genetic algorithm (IGA). To evaluate this approach, we compare the CPU and fitness values of the IGA to the results provided by an adapted genetic algorithm to find the shortest Hamiltonian circuit in a superimposed graph.

within constraint satisfaction problems (CSPs), our work proposes a new search heuristic. The latter is basically inspired by the template concept introduced by E.P.K. TSANG. It consists of a new template concept and could be classified as a min-conflict heuristic. In this paper we introduce a new guided local search that uses our heuristic in local search (LS) for Maximal constraint satisfaction problems (Max-CSPs). Compared to previous well known guided local search, our approach changes experimental results for the better.

within constraint satisfaction problems (CSPs), in this paper we introduce a honeybee algorithm that uses new template concept for image segmentation. Our approach uses the guided local search and the graph theory to achieve an improved image segmentation. Compared to previous well known heuristics, our approach is giving better experimental results.

The Multi-Robot Task Allocation (MRTA) problem has recently become a key research topic. Task allocation is the problem of mapping tasks to robots, such that the most appropriate robot is selected to perform the most fitting task, leading to all tasks being optimally accomplished. Expanding the number of tasks and robots may cause the collaboration among the robots to become tougher. Since this process requires high computational time, this paper describes a technique that reduces the size of the explored state space, by partitioning the tasks into clusters. In real-world problems, the absence of information regarding the number of clusters is ordinarily occurring. Hence, a dynamic clustering is auspicious for partitioning the tasks to an appropriate number of clusters. In this paper, we address the problem of MRTA by putting forward a new simple, automatic and efficient clustering algorithm of the robots’ tasks based on a dynamic distributed particle swarm optimization, namely, ACD 2 PSO. Our approach is made out of two stages: stage I groups the tasks into clusters using the dynamic distributed particle swarm optimization (D 2 PSO) algorithm and stage II allocates the robots to the clusters. The assignment of robots to the clusters is represented as multiple traveling salesman problems (MTSP). Computational experiments were carried out to prove the effectiveness of our approach in term of clustering time, cost, and the MRTA time, compared to the distributed particle swarm optimization (dPSO) and genetic algorithm (GA). Thanks to the D 2 PSO algorithm, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing the fast convergence of PSO.

Communication security has taken vital role with the advancement in digital communication. The universal use of internet for communication has increased the attacks to users. The security of information is the present issue related to privacy and safety during storage and communication. This paper deals with the proposition of a multilayered secure channel to transfer sensitive data/video over an unreliable network. The secret video is first encrypted using the NOLSB algorithm. The cipher video produced is hidden in a video file with more size. This video file is in turn encrypted following the (m,k) firm technique to maximize resource utilization and to optimize the bandwidth. Then video shares are sent over many channels in the network in order to assure security. This method guarantee that, even if some shares got lost over network, video files could be recovered at the end receiver without need to resend the video file by the sender.

The dynamism of web service on the internet brings new needs to build autonomous services. In practical environments, Web services could occur many incidents with unacceptable information and generate a large number of undesirable solutions. Thus, it becomes an additionally urgent challenge on how to adjust service-based applications to meet highly dynamic environments and fast-changing business requirements. A self-reconfigurable application becomes a necessity to produce an adequate web service process. To cope with this hindrance, the present paper proposes a sensor and repair-based algorithm to achieve reliability at the component level before a system crash occurs. We adopt a new sensor method based on project management rules to detect the faulty region on a workflow. After that, we applied the repair algorithm: such as a single service (SSR) and multiple service reconfiguration (MSR) that systematically search to find a new nearby solution. In MSR algorithm we referred to a new multi-level VCSOP with Harmony Search (HS) metaheuristic to quickly recover the faulty region without system crash. The results show that our proposed method could recover the original instance of the faulty region without user integration and in a very acceptable time regarding the recomposing process.

This paper work deals with the presentation of an hybrid genetic approach which allocates and schedules, on a multiprocessor architecture, a system of real-time tasks while balancing the load on the processors. In addition, this hybrid heuristic approach takes into account the safety critical applications which is the focus of our work. We have carried out a performance analysis showing that the proposed hybrid genetic approach has results better than the classical GAs in terms of load balancing, minimum response time and good flexibility.

Nowadays, scientific progress in multiple disciplines gave rise to conducting large scale scientific experiments and applications known as High Performance Computing (HPC). These HPC applications are commonly structured as workflows of heavy tasks with large data size and intricate dependencies which are typically performed in large-scale distributed systems (LSDS) such as clusters, Grids and recently Cloud infrastructures. In fact, workflow scheduling in distributed systems, especially in Clouds, is proved to be an NP hard problem. Our main target in this paper is to design a workflow scheduling approach based on the Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II) in hybrid distributed systems by optimizing the Makespan and cost. In this work, we also studied the improvement of the Makespan-Cost trade-off with the scalability concept in the Cloud with our designed approach. For that, we proposed different scenarios dealing with the provisioning strategy of processing nodes alongside an existing resource pool. Conducted experiments show the advantage of Cloud infrastructures against other distributed systems and allow investigating the different factors in correlation with resources provisioning process.

In this work, we are interested in a variant of DisCSPs which is distributed optimization problems under fuzzy constraints (DisFCSPs: Distributed Fuzzy Constraint Satisfaction Problems). This problem lies at the confluence of two areas of research: Distribute d optimization and fuzzy optimization. Fuzzy optimization by metaheuristics, and more particularly by genetic algorithms, is well studied in the centralized case, contrary to the decentralized case. We present a new multi-agent framework designed to create fuzzy distributed constraint satisfaction (DisFCSP) problems and their resolution using population based metaheuristics. Different types of agents (interface agents, mediating agent, proposing agents and negotiating agents) interact in a cooperative architecture to receiv e, read and deliver information about problems, coordinate activities, build promising solutions and generate improved solutions, realizing a metaheuristic process as a collaborative behavior. The genetic algorithm (GA) is able to exploit the architecture and can be easily described in the proposed framework.

This paper introduces a new distributed approach to solve multi-objective planning problem applied to multimodal transport network planning (MTNP) problem. In this problem, the commodities should be transported on the international network by at least two different transport modes. The main goal is to find the best multimodal transportation modes and itineraries. The aim of the new approach has assured us that a distributed optimization. We split the MTNP problem into two sub-problems. These sub-problems are the assignment and the planning problems. Each sub-problem is solved at a corresponding layer. Each layer is executed by an agent. These agents interact, collaborate and communicate together to solve the MTNP problem. In this paper, we contribute by introducing a multi-layer distributed approach to solve real case’s problems. Firstly, we define the MTNP problem as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). Secondly, we show that the split of the main problem reduces the computational complexity and the communication between the planner agent and the modes agents lead to faster convergence. The experimental results are proof of this work efficiently. This method proves their efficiency, according to the complexity of the problem and the exchange of information, the computational time and the solution quality.

Constraint satisfaction problems (CSPs) are globally used to perform real life problems. They are dealt with many optimization complete and incomplete approaches. In this context nature inspired methods are very successful. In our work and within this context, we introduce a new guided honey bee optimization algorithm for Maximal constraint satisfaction problems (Max-CSPs). The guidance consists of a new template concept and could be classified as a min-conflict heuristic. Our new template concept overcomes the former template concept guidance problems. It makes it possible to apply a fair min conflict heuristic. Compared to previous well-known honeybee algorithms for Max-CSPs, our approach is clearly better in term of CPU time and in term of solution quality.

Wireless sensor networks (WSNs) consists of a large number of sensor nodes to monitor physical or environmental conditions. Each sensor node has specific tasks to do with its neighbours. On the other hand, if node does not perform it, it is considered as misbehaviour node and often interrupt the normal functionality of a WSN. In order to keep functional WSNs, it is necessary to identify the misbehaviour of the node, which will save the network from internal attacks. So, an effective security mechanism is essential. The work presented in this paper is concerned with introduced trust management based Watermarking-LEACH to prevent at the same time internal and external attacks. This leads to introduce a new trust model into Watermarking-LEACH schema to infer the total trust and security with the energy-efficiency. To this end, the implementation and simulation of T-W-LEACH approach is done with the MATLAB tool simulator and the evaluation of security and energy is made to perform a comparative with TBE- LEACH a Trust based energy efficient routing in LEACH for wireless sensor networks.

The huge amount of exchanged data in the internet between entities and the quick development of edge computing has increased users frustration about the future generations. We expect a future where transactions will be based on clouds and virtual machines, gathered from sensors and IoT devices and processed by different artificial intelligence algorithms or agents. The speed and backlash change in the world has driven researchers to work on privacy and security for entities’ information and transactions. Different concepts arise and still in their early stage as digital identity, self sovereign identity, global unique identifier and identity of things (IDoT). Therefore, motivated by the recent explosion of interest around Blockchains, we examine whether they make a good fit for the Identity Internet of Things (IDoT) sector. Blockchain a major distributed peer-to-peer network where non-trusting members can interact together without a need for a trusted third party, it actually make available many advantages to providers and consumers and solve data protection features lifelong a transaction existing. As being immutable, transparent and trustful platform the Blockchain allows managing identities and privacy of its nodes information. Our contribution is a new architecture eventually using a public Blockchain and creating a smart identity wallet. It contains standard node data and will also integrate proactive data behavior and the reactive one. The main target is to protect users from sybil attack at early stage. We will define a new digital wallet based on entities behavior in order to prevent 51% attack and Sybil attack

We studied in this article a topic that focused on two areas of research: Constraint Satisfaction Problems (CSP) and genetic algorithms. The problem is that this type of algorithm is recognized to be greedy in terms of CPU time. To solve this problem, we tried to integrate the arc consistency (AC) at the initial population in a way that it would be the result of this filtering. First, we generated the genetic algorithm without integrating the arc consistency. Then, we considered that each chromosome is a CSP, each gene is a variable of the problem and each allele represents the taken value. We randomly generated the CSP to obtain the inconsistent values of each pair of variables. To remove these values, we used the technique of arc consistency as a technique for solving this type of problem, that means we have worked to eliminate from each variables domain the values which violate the constraint specific and make the CSP inconsistent. The aim of this work is to reduce performance in terms of execution time of the genetic algorithm.

Thanks to the appearance of the General-Purpose computing on Graphics Processing Units (GPGPU), researchers have benefited from the spectacular High Performance Computing (HPC) provided by GPUs. Different research fields, such as combinatorial optimization, have taken advantages from the GPUs HPC. In this context, our paper introduces some different Particle Swarm Optimization (PSO) implementations for solving Maximal-Constraint Satisfaction Problems (Max CSPs) using GPU, based on different parallelism levels. These implementations are then compared. The experimental results, presented at the end, show the effectiveness and the efficiency of using GPU to optimize Max-CSPs by PSO.

Path planning problem for large number of robots is a quite challenging problem in mobile robotics since their control and coordination becomes unreliable and sometimes unfeasible. Particle Swarm Optimization (PSO) has been demonstrated to be a useful technique in the field of robotic research. This paper discusses an optimal path planning algorithm based on a Dynamic Distributed Particle Swarm Optimization Algorithm (D2PSO). The purpose of this approach is to find collision free optimal paths using two local optima detectors. This would add diversity to the population and hence avoid stagnation problem. The results show that the D2PSO has a better ability to get away from local optimums than the distributed PSO (dPSO). Simulations prove that this methodology is effective for every robot in multi-robot framework to discover its own proper path from the start to the destination position with minimum distance and no collision with obstacles.

Designing of the composite services raises many challenges, as the huge number of equivalent ones and the complexity of user’s requirements. Thus, in order to achieve user satisfaction and problem resolution, we have to choose among a set of functionally equivalent services. So, the composition can be regarded as a valued constraint satisfaction optimization problem, which involved a valuation of requirements attributes. Next, the skyline operator was applied to our search space. Skyline is an operator based on Pareto dominance, used to help in reducing the size of a search space for many applications such as web service composition. However, this technique has some drawbacks such as the important number of solutions which it couldn’t, usually, consider all users’ preferences or context. In this paper, we propose a new approach based on local consistency reinforcement methods (node and arc-consistency), to enhance the skyline approach and to reach the user needs. The idea is to keep only the highest service quality in the skyline set. Then, the Harmony Search algorithm adopted to catch an optimal or near-optimal composition. The efficiency of these algorithms is empirically studied, showing the capacity of our approach in reducing skyline size and keeping only the personalized services.

This study suggests a multi-objective firework algorithm based on the Pareto-dominance method in order to select the optimistic solution from the multimodal transportation problem. The aim is to determine a shortest and efficient itinerary of satisfying a certain set of demands and operational constraints. Several publications have appeared in recent years documenting the transportation network analysis. This fact reflects the importance and complexity of this problem. In order to reach a sustainable planning in a rather complicated transport system, it is of high interest to use an approach that belongs to the field of artificial intelligence method. This paper introduces a new multi-objective approach based on firework algorithm that generates n solutions which are then selected according to the Pareto-dominance which is the best compromise between goals. Finally, we present our obtained experimental results, then we compare them with the obtained results by Cplex in order to prove the efficiency of this algorithm.

Wireless Sensor Networks (WSNs) technologies have been successfully applied to a great variety of outdoor scenarios but, in practical terms, little effort has been applied for indoor environments, and even less in the field of industrial applications. This paper work presents an intelligent hybrid WSN application for an indoor and industrial scenario, with the aim of improving and increasing the levels of human safety and to avoid the denial of service (DoS) attacks. Since its operates on a wireless network, an adversary can always perform a DoS attack by jamming the radio channel with a strong signal. The main contribution of our work is to use a hybrid approach that handles the problem of jamming. The proposed solution improves security by protecting against DoS and returns near-optimal solutions. The paper shows the viability of our approach in terms of performance, scalability, modularity and safety.

Constraint Satisfaction Problems (CSPs) occur now in different domains. Several methods are used to solve them. In particular, Particle Swarm Optimization (PSO) allows to solve efficiently CSPs by significantly reducing the calculation time to explore the search space of solutions. However, this metaheuristic is excessively costing when facing large instances. In this paper we address the Maximal Constraint Satisfaction Problems (Max-CSPs). We introduce a new resolution approach that allows solving efficiently the Max-CSPs even with large instances. Our purpose is to implement a PSO based method by using the GPU architecture as a parallel computing framework. In particular, we focus on the implementation of two parallel novel approaches. The first one is a parallel GPU-PSO for Max-CSPs (GPU-PSO) and the second one is a GPU distributed PSO for Max-CSPs (GPU-DPSO). Our experimental results show the efficiency of the two proposed approaches and their ability to exploit GPU architecture.

The Bin Packing Problem's purpose (BPP) is to find the minimum number of bins needed to pack a given set of objects of known sizes so that they do not exceed the capacity of each bin. This problem is known to be NP- Hard. In this paper, we propose an hybrid genetic algorithm using BFD (Best Fit Decreasing) to deal with infeasible solution due to the bin-used representation. Experimental results showed the effectiveness of our approach for infeasible chromosomes thereby improving the quality of the obtained solution. This will give a good result for the virtual machine placement in Cloud to minimize energy since it looks like a BPP.

The multidimensional multi-choice knapsack problem (MMKP) is NP-hard. Within the framework of solving this problem, we suggest newer approaches. We not only propose a multi-starts version of our previous works approach using surrogate constraint informations based choices [31][32], but also we introduce another newer heuristic. The latter uses Lagrangian relaxation informations in place of surrogate informations. Compared with other literature known methods described so far, our approaches experimentations results are competitive.

In this paper we present a new heuristic for solving the multidimensional multi-choice knapsack problem called MMKP. The main idea is to explore both sides of the feasibility border that consists in alternating both constructive and destructive phases in a strategic oscillating manner. Performance analysis of the method shows the merits of using surrogate constraint information as choice rules for solving this problem class. A constraint normalization method is also used to strengthen the surrogate constraint information in order to improve the computational results. Numerical results show that the performance of this approach is competitive with previously published results.

D3G2A is a new multi-agent approach which addresses additive constraint satisfaction problems (SigmaCSPs). This approach is inspired by the guided genetic algorithm (GGA) and by the dynamic distributed double guided genetic algorithm for Max_CSPs. It consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA. First, our approach will be enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. In the second step, the agents performed GAs will, no longer be the same. This is stirred by NEO-DARWINISM theory and the nature laws. In fact the new algorithm will let the species agents able to count their own GA parameters. In order to show D3G2A advantages, the approach and the GGA are applied on the radio link frequency allocation problem (RLFAP). The experimental comparison is provided

Inspired by the guided genetic algorithm (GGA) and by the dynamic distributed double guided genetic algorithm for Max/spl I.bar/CSPs, D/sup 3/G/sup 2/A is a new multi-agent approach which addresses additive constraint satisfaction problems (/spl Sigma/CSPs). This algorithm consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA, guided by both the template concept and the min-conflict-heuristic. In one hand, genetic algorithms (GAs) efficiency provides good solution quality for additive CSPs and, in the other hand, multi-agent principles reduces GA temporal complexity. First, our approach is enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. In the second step, the performed GAs no longer have the same cross-over and mutation probabilities. This is done on the basis of NEO-DARWINISM theory and the nature laws. In fact the new algorithm let the species agents able to count their GA parameters. In order to show D/sup 3/G/sup 2/A advantages, experimental comparison with GGA is provided.