El-Ghazali Talbi

University of Lille Nord de France · Polytech-lille

Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new approach based on Chaotic search to solve MOPs. Various Tchebychev scalarization strategies have been investigated. Moreover, a comparison with state of the art algorithms on different well known bound constrained benchmark...

In the near future, more than two thirds of the world’s population is expected to be living in cities. In this interconnected world, data collection from various sensors is eased up and unavoidable. Handling the right data is an important factor for decision making and improving services. While at the same time keeping the right level of privacy fo...

Protein structure prediction is an essential step in understanding the molecular mechanisms of living cells with widespread application in biotechnology and health. The inverse folding problem (IFP) of finding sequences that fold into a defined structure is in itself an important research problem at the heart of rational protein design. In this cha...

Multi-objective optimization under uncertainty has gained considerable attention in recent years due to its practical applications in real-life. Many studies have been conducted on this topic, but almost all of them transformed the problem into a mono-objective one or just neglected the effects of uncertainty on the outcomes. This paper addresses s...

Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address combinatorial multi-objective problem under uncertainty using the possibilistic framework. To thi...

Multi-objective optimization problems with more than three objectives, which are also termed as many objective optimization problems, play an important role in the decision making process. For such problems, it is computationally expensive or even intractable to approximate the entire set of optimal solutions. An alternative is to compute a subset...

This paper considers a multi-objective variant of vehicle routing problems, in which the customer demands are supposed to be triangular fuzzy numbers and the objective functions are also disrupted by fuzziness. However, the propagation of fuzzy demands to the objectives can affect the reliability of generated solutions. To this end, we propose a ro...

The paper addresses the robustness of multi-objective optimization problems with fuzzy data, expressed via triangular fuzzy numbers. To this end, we introduced a new robustness approach able to deal with fuzziness in the multi-objective context. The proposed approach is composed of two main contributions: First, new concepts of \(\beta \)-robustnes...

In this paper an iterated local search (ILS) is embedded with a variable neighborhood Descent (VND) hyper-heuristic. The proposed hyper-heuristic combines low-level heuristics. Several variants from the literature within the proposed ILS were implemented and tested. This article conducts an empirical study involving hard combinatorial optimization...

This book discusses the main techniques and newest trends to manage and optimize the production and service systems. The book begins by examining the three main levels of decision systems in production: the long term (strategic), the middle term (tactical) and short term (operational). It also considers online management as a new level (a sub level...

We discuss the role of uncertainty in the resource/service provisioning, investment, operational cost, programming models, etc. that have not yet been adequately addressed in the scientific literature. Clouds differ from previous computing environments in the way that they introduce a continuous uncertainty into the computational process. The uncer...

In this chapter, a clear difference is made between the parallel design aspect and the parallel implementation aspect of evolutionary algorithms (EAs). From the algorithmic design point of view, the main parallel models for EAs are presented. A unifying view of parallel models for EAs is outlined. This chapter is organized as follows. In Sect. 55.2...

This paper introduces two new nominal NK Landscape model instances designed to mimic the properties of one challenging optimisation problem from biology: the Inverse Folding Problem (IFP), here focusing on a simpler secondary structure version. Through landscape analysis tests, numerous problem properties are identified and used to param-eterise an...

Cloud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at...

In spite of extensive research of uncertainty issues in different fields ranging from computational biology to decision making in economics, a study of uncertainty for cloud computing systems is limited. Most of works examine uncertainty phenomena in users’ perceptions of the qualities, intentions and actions of cloud providers, privacy, security a...

The Adaptive Enhanced Distance Based Broadcasting Protocol, AEDB hereinafter, is an advanced adaptive protocol for information dissemination in mobile ad hoc networks (MANETs). It is based on the Distance Based broadcasting protocol, and it acts differently according to local information to minimize the energy and network use, while maximizing the...

This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into pre-booked resources from a cloud broker, maximizing the broker profit. Three metaheuristic are studied: Simulated Annealing, Genetic Algorithm, and hybrid Evolutio...

In the single objective Unit Commitment Problem (UCP) the problem is usually separated in two sub-problems : the commitment problem which aims to fix the on/off scheduling of each unit and the dispatching problem which goal is to schedule the production of each turned on unit. The dispatching problem is a continuous convex problem that can easily b...

In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding Problem (IFP) is in itself an important research problem, but also at the heart of most rational protein design approaches. In brief, the IFP cons...

In this paper, we present a new energy efficiency model and architecture for cloud management based on a prediction model with Gaussian Mixture Models. The methodology relies on a distributed agent model and the validation will be performed on OpenStack. This paper intends to be a position paper, the implementation and experimental run will be cond...

Over the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming and machine...

The satellite communications market is competitive and rapidly evolving. The payload, which is in charge of applying frequency conversion and amplification to the signals received from Earth before their retransmission, is made of various components. These include reconfigurable switches that permit the re-routing of signals based on market demand...

The articles in this special issue are dedicated to computational intelligence for Cloud computing. Cloud computing systems represent an emerging technology which allows its users to have access to large scale, efficient and highly reliable computing systems by paying according to their needs. Cloud computing generally consists of a heterogeneous s...

In this article, we propose to apply a hybrid method called DYNAMOP (DYNAmic programming using Metaheuristic for Optimization Problems) to solve the Unit Commitment Problem (UCP). DYNAMOP uses a representation based on a path in the graph of states of dynamic programming, which is adapted to the dynamic structure of the problem and facilitates the...

In this paper, we build upon the previous efforts to enhance the search ability of Moead (a decomposition-based algorithm), by investigating the idea of evolving the whole population simultaneously at once. We thereby propose new alternative selection and replacement strategies that can be combined in different ways within a generic and problem-ind...

Solving to optimality large instances of combinatorial optimization problems using Brand and Bound (B&B) algorithms requires a huge amount of computing resources. In this paper, we investigate the design and implementation of such algorithms on computational grids. Most of existing grid-based B&B algorithms are based on the Master-Worker paradigm,...

Energy efficiency is one of the major concerns when scheduling large workload on current large computing systems including cluster, grid, and cloud computing environments. This article presents a two-level hierarchical strategy to schedule large workloads of parallel applications on multicore distributed systems with the aim of concurrently minimiz...

Ranking fuzzy numbers is an important aspect in dealing with fuzzy optimization problems in many areas. Although so far, many fuzzy ranking methods have been discussed. This paper proposes a new Pareto approach over triangular fuzzy numbers. The approach is composed of two dominance stages. In the first stage, mono-objective dominance relations are...

Local search based algorithms are a general and computational efficient metaheuristic. Restarting strategies are used in order to not be stuck in a local optimum. Iterated local search restarts the local search using perturbator operators, and the variable neighbourhood search alternates local search with various neighbourhood sizes. These two popu...

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a large-scale cloud. Minimizing energy consumption can significantly reduce the amount of energy bills and the greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less ene...

A hybrid metaheuristic based solution is proposed to solve the annual optimal hydro generation scheduling problem. The problem of the hydro generation scheduling is formulated as a continuous non-linear optimization problem and solved using enhanced combination of metaheuristics: random greedy, evolutionary algorithm, and a pseudo dynamic programmi...

Cloud computing has emerged during the last decade to be widely adopted nowadays in several IT areas. It consists to propose market or not market-oriented resources as services that can be consumed in a ubiquitous, flexible and transparent way. In this paper, we deal with scheduling, one of the major cloud computing issue. According to the targeted...

In this article, we propose DYNAMOP (DYNAmic programming using Metaheuristic for Optimization Problems) a new dynamic programming based on genetic algorithm to solve a hydro-scheduling problem. The representation which is based on a path in the graph of states of dynamic programming is adapted to dynamic structure of the problem and it allows to hy...

In this paper, a multi-objective 2-dimensional vector packing problem is presented. It consists in packing a set of items, each having two sizes in two independent dimensions, say, a weight and a length into a finite number of bins, while concurrently optimizing three cost functions. The first objective is the minimization of the number of used bin...

In this chapter, the authors study the dynamic extension of the conventional vehicle routing problem (VRP) and its resolution by a recent metaheuristic paradigm. The VRP consists of determining, by minimizing the route cost, a set of routes for a limited number of vehicles, beginning and ending at a depot, such that each customer is visited exactly...

In real-world manufacturing environments, it is common to face a job-shop scheduling problem (JSP) with uncertainty.Among different sources of uncertainty, processing times uncertainty is the most common. In this paper, we investigate the use of a multiobjective genetic algorithm to address JSPs with uncertain durations. Uncertain durations in a JS...

Graph coloring was exploited in wireless sensor networks to solve many optimization problems. These problems are related in general to channel assignment. In this paper, we propose to jointly use coloring for routing purposes. We introduce CHRA a coloring based hierarchical routing approach. Coloring is exploited to avoid interferences and also to...

In this chapter, the authors propose a multiobjective model for the biclustering problem applied to microarray data. The chapter presents two hybrid multiobjective algorithms called MOBInsga and MOBIjbea which are based on the well-known metaheuristics NSGA-II and IBEA. The chapter provides definitions related to biclustering, and explains multiobj...

This paper presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search metaheuristics: ParadisEO-MO. A substantial number of single solution-based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grai...

This article presents a new parallel hybrid evolutionary algorithm to solve the problem of virtual machines subletting in cloud systems. The problem deals with the efficient allocation of a set of virtual machine requests from customers into available pre-booked resources from a cloud broker, in order to maximize the broker profit. The proposed par...

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increase the provider’s profit. In addition, the reduction of energy decreases greenhouse gas emissions. T...

In this paper, we address a bi-objective 2-dimensional vector packing problem (Mo2-DBPP) that calls for packing a set of items, each having two sizes in two independent dimensions, say, a weight and a height, into the minimum number of bins. The weight corresponds to a "hard" constraint that cannot be violated while the height is a "soft" constrain...

This article introduces the formulation of the VirtualMachine Planning Problem in cloud computing systems. It deals with the efficient allocation of a set of virtual machine requests from customers into the available pre-booked resources the broker has in a number of cloud providers, maximizing the broker profit. Eight list scheduling heuristics ar...

During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constrai...

The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were proposed to this end, including classical resource allocation heuristics, machine learning or stochastic optimization. No consensus exists but a trend...

In this paper, we deal with cloud brokering for the assignment optimization of VM requests in three-tier cloud infrastructures. We investigate the Pareto-based meta-heuristic approach to take into account multiple client and broker-centric optimization criteria. We propose a new multi-objective Genetic Algorithm (MOGA-CB ) that can be integrated in...

Many real-world decision-making situations possess both a discrete and combinatorial structure and involve the simultaneous consideration of conflicting objectives. Problems of this kind are in general of large size and contains several objectives to be “optimized”. Although Multiple Objective Optimization is a well-established field of research, o...

The exact resolution of large instances of combinatorial optimization problems, such as three dimensional quadratic assignment problem (Q3AP), is a real challenge for grid computing. Indeed, it is necessary to reconsider the resolution algorithms and take into account the characteristics of such environments, especially large scale and dynamic avai...

In recent years, the application ofmetaheuristic techniques to solve multilevel and particularly bi-level optimization problems (BOPs) has become an active research area. BOPs constitute a very important class of problems with various applications in different domains. A wide variety ofmetaheuristics have been proposed in the literature to solve su...

This book provides a complete background on metaheuristics to solve complex bi-level optimization problems (continuous/discrete, mono-objective/multi-objective) in a diverse range of application domains. Readers learn to solve large scale bi-level optimization problems by efficiently combining metaheuristics with complementary metaheuristics and ma...

In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to the complexity of MOSPs, an efficient approximation...

Public cloud computing allows one to rent virtual servers on a hourly basis. This raises the problematic of being able to decide which server offers to take, which providers to use, and how to use them to acquire sufficient service capacity, while maintaining a cost effective platform. This article proposes a new realistic model to tackle the probl...

This paper deals with multi-objective problems under uncertainty, using the possibilistic framework which offers a simple and natural way to express uncertainty underlying most of real-world problems. To this end, we propose new Pareto relations for ranking the generated triangular fuzzy solutions in both mono-objective and multi-objective cases. T...

In this paper a Variable Neighborhood Descent (VND) approach, is developed to solve the Single Machine Total Weighted Tardiness Problem (SMTWTP). New strategy was proposed to select iteratively the accurate neighborhood. Our approach was compared to VND state-of-the-art approaches. Statistical tests were also applied on the empirical results, to sh...

In this work we deal with a multiobjective biclustering problem applied to microarray data. MOBI nsga [21] is one of the multiobjective metaheuristics that have been proposed to solve a new multiobjective formulation of the biclustering problem. Using MOBI nsga , biclusters of good quality can be extracted. However, the generated front approximatio...

In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each...

Over the last decade, there has been a growing interest in the use of graphics processing units (GPUs) for non-graphics applications. From early academic proof-of-concept papers around the year 2000, the use of GPUs has now matured to a point where there ...

Local search metaheuristics (LSMs) are efficient methods for solving complex problems in science and industry. They allow significantly to reduce the size of the search space to be explored and the search time. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. Therefore, the use of GPU-based massively...

This paper presents a comparative study of multi-objective evolutionary algorithms on the bi-objective 2-dimensional vector packing problem. Three state-of-the-art methods which prove their efficiency for a large variety of multi-objective optimization problems were designed to approximate the whole Pareto set of the problem. Computational experime...

Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the Dynamic Vehicle Routing Problem (DVRP), which i...

Over the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization. The best results found for many real-life or classical optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming, constraint programming and machine learning technique...

Existing models from scheduling often over-simplify the problems appearing in real-world industrial situations. The original application is often reduced to a single-objective one, where the presence of uncertainty is neglected. In this paper, we focus on multi-objective optimization in uncertain environments. A bi-objective flowshop scheduling pro...

In the classical bin-packing problem with conflicts (BPC), the goal is to minimize the number of bins used to pack a set of items subject to disjunction constraints. In this paper, we study a new version of BPC: the min-conflict packing problem (MCBP), in which we minimize the number of violated conflicts when the number of bins is fixed. In order...

Hybrid metaheuristics are powerful methods for solving complex problems in science and industry. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. As a result, the use of GPU computing has been recognized as a major way to speed up the search process. However, most GPU-accelerated algorithms of the lit...

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a cloud distribution dispatched over a huge number of machines. Minimizing energy consumption can significantly reduce the amount of energy bills, and the greenhouse gas emissions. Therefore, many researches are carried out to deve...