Raja Fdhila

Distributed MOPSO with a new population subdivision technique for the feature selection

In this paper, a new Multi-Objective Particle Swarm Optimization (MOPSO) is applied to solve a problem of feature selection defined as a multiobjective problem. This algorithm (pMOPSO), known for its fast convergence with negligible computation time is based on a distributed architecture. Sub-swarms are obtained from dynamic subdivision of the population using Pareto Fronts. The algorithm addresses a problem defined by two goals, characterized by their contradictory aspect, namely, minimizing the error rate and minimizing the number of features. The two objectives are treated simultaneously constituting the objective function. Performance of our approach is compared with other evolutionary techniques using databases choosing from the UCI repository [1].

A multi objective particles swarm optimization algorithm for solving the routing pico-satellites problem

This paper belongs to the field of communication and computer networks. Networks of low earth orbiting satellites are able to provide wireless connectivity to any part of the world while ensuring timely and better performance lower bit error rate. This type of technology has been growing interest towards the development of small satellites. Especially, when we talk about the execution of the service quality system, we must use some optimization techniques. However, these systems have the drawback of energy management which is the biggest problem to worry about. Therefore, optimization of the processing time and the effective implementation of information flow and storage on board must be discussed with respect to topology changes fast. In this paper we will discuss various routing algorithms of data used in small satellites and terrestrial networks. As a multiobjective problem, we try to solve the problem of routing data with multiobjective particle swarm optimization (MOPSO).

A new hierarchical approach for MOPSO based on dynamic subdivision of the population using Pareto fronts

This paper introduces a new hierarchical architecture for multi-objective optimization. Based on the concept of Pareto dominance, the process of implementation of the algorithm consists of two stages. First, when executing a multiobjective Particle S warm Optimization (MOPSO), a ranking operator is applied to the population in a predefined iteration to build an initial archive Using ε-dominance. Second, several runs will be based on a dynamic number of sub-populations. Those populations, having a fixed size, are generated from the Pareto fronts witch are resulted from ranking operator. A comparative study with other algorithms existing in the literature has shown a better performance of our algorithm referring to some most used benchmarks.

Single- and multi-objective particle swarm optimization of reservoir structure in Echo State Network.

Echo State Networks ESNs are specific kind of recurrent networks providing a black box modeling of dynamic non-linear problems. Their architecture is distinguished by a randomly recurrent hidden infra-structure called dynamic reservoir. Coming up with an efficient reservoir structure depends mainly on selecting the right parameters including the number of neurons and connectivity rate within it. Despite expertise and repeatedly tests, the optimal reservoir topology is hard to be determined in advance. Topology evolving can provide a potential way to define a suitable reservoir according to the problem to be modeled. This last can be mono- or multi-constrained. Throughout this paper, a mono-objective as well as a multi-objective particle swarm optimizations are applied to ESN to provide a set of optimal reservoir architectures. Both accuracy and complexity of the network are considered as objectives to be optimized during the evolution process. These approaches are tested on various benchmarks such as NARMA and Lorenz time series.

Distributed MOPSO with dynamic pareto front driven population analysis for TSP problem

This paper describe the use of Multi-Objective concept to solve the Traveling Salesman Problem (TSP). The traveling salesman problem is defined as an NP-hard problem. The resolution of this kind of problem is based firstly on exact methods and after that is based on single objective based methods as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Firstly, a short description of the Multi-objective Particles swarm optimization (MOPSO) is given as an efficient technique to use for many real problems. Based on the concept of Pareto dominance, the process of implementation of the algorithm consists of two stages. First, when executing a multi-objective Particle Swarm Optimization (MOPSO), a ranking operator is applied to the population in a predefined iteration to build an initial archive using ε-dominance The TSP problem is characterized by two contradictory objectives as minimize the total distance traveled by a particle and minimize the total time. An experimental study is conducted in this paper. A comparative study with other algorithms existing in the literature has shown a better performance of our algorithm (pMOPSO).

Hierarchical design for distributed MOPSO using sub-swarms based on a population Pareto fronts analysis for the grasp planning problem

This paper discusses the use of intelligent technology to solve the problem of grasp planning known as a difficult problem. The scope aims to find points of contact between a five-fingered hand and an object. In this paper, we applied a new hierarchical approach for distributed Multi-Objective Particles Swarms Optimization, based on dynamic subdivision of the population using Pareto fronts (pbMOPSO) for the optimization of the grasp planning problem. The problem is based on simultaneous optimization of two objectives functions. The first objective is to explore the space of skillful manipulation of a robot hand with five fingers and find the best configuration of the fingers by minimizing the distance between the center of mass of the object and the center of the contact polyhedron. The second evaluation function is to maximize another quality measure that is related to the angles defining a configuration of the hand. An experimental study done with the HandGrasp simulator has shown a better performance of our algorithm to solve the grasp planning problem.

Optimization algorithms, benchmarks and performance measures: From static to dynamic environment

This paper is a tentative to describe the basics of dynamic optimization using swarm & evolutionary methods. Computational intelligence methods based on swarming, collaborative computing and related techniques showed their potentials at solving classical static problems; for dynamic problems new paradigms needs to be established, this concerns the methods, the test benches and the performance evaluation processes. A review of the key population based computational techniques is performed prior to set some perspective guidelines on how to handle the multi-objective dynamic problems using these technique.

PSO based data routing in a networked distributed Pico-satellites system

Low earth orbit satellites networks are capable of providing wireless connectivity to any part of the world while guaranteeing lower delays and better bit error rate performance. During the last years, there have been many growing interests towards the development of small satellites. Using these miniaturized systems as a cooperating network allows cost saving and a short development period. However, these systems present the drawback of energy management which is the most important problem to care about. Therefore, the optimization of the processing time and the efficient implementation of the information flow and storage on board must be discussed with respect to the quick topology changes. In this paper, we discuss the use of a network of Pico-satellites with distributed tasks instead of one big satellite to get a fault-tolerant and a robust system, this will be helpful for cost saves and development time reducing. Thus, we have to consider an intelligent system able to achieve several space missions such as attitude control, telemetry and communication. So, we present as a first step different routing algorithms used in small satellites and terrestrial networks. In addition, we present the proposed simulation system which is based on satellite position prediction, satellites tracking software and the use of a Particle Swarm Optimisation (PSO) as an intelligent technique for information assignment. As a result, the simulation shows the importance of the use of intelligent techniques in space application due to the complicated environment and the lack of energy sources compared to the earth networks. In deed, The PSO solution avoids the use of one satellite in a critical state and generates a good quality of service when big data traffic in the network occurs, that is because of sharing and distributing the whole traffic process. This solution allows the sustainability of the network and a better Quality of service.

MOPSO for dynamic feature selection problem based big data fusion

Optimization process occurs in many aspects and areas of everyday life. However, the big use of the internet in recent years caused a complex management of large quantities of data that are stored in many different data sources and optimization attend the domain of big data to optimize multi and dynamic data that stored in a complex dataset including all types of transactions in the data sources. So, the diversity of data stored in different data sources caused a complexity to access the information and user find a problem to present the same real world object from different sources in a clear and complementary one representation. Therefore, the high complexity of the representation of a target concept “object” that provided from different data sources, the dynamic feature selection problem based big data fusion present as a solution and a novel approach that will be applicable to solve a dynamic multi-objective optimization feature selection problem (MOOP) based on Multi-Objective Particle Swarm Optimization (MOPSO). This paper carried out on the state-of-the-art of the research done to present an overview of static and dynamic optimization in literature approach, then to define an overview of big data and to present an idea about the future work that will be able to solve the dynamic feature selection based on big data fusion with MOPSO.

Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation

This paper investigates a new hybridization of multi-objective particle swarm optimization (MOPSO) and cooperative agents (MOPSO-CA) to handle the problem of stagnation encounters in MOPSO, which leads solutions to trap in local optima. The proposed approach involves a new distribution strategy based on the idea of having a set of a sub-population, each of which is processed by one agent. The number of the sub-population and agents are adjusted dynamically through the Pareto ranking. This method allocates a dynamic number of sub-population as required to improve diversity in the search space. Additionally, agents are used for better management for the exploitation within a sub-population, and for exploration among sub-populations. Furthermore, we investigate the automated negotiation within agents in order to share the best knowledge. To validate our approach, several benchmarks are performed. The results show that the introduced variant ensures the trade-off between the exploitation and exploration with respect to the comparative algorithms.