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International audience; Mixed Integer Linear Programs (MILPs) are usually NP-hard mathematical programming problems, which present difficulties to obtain optimal solutions in a reasonable time for large scale models. Nowadays, metaheuristics are one of the potential tools for solving this type of problems in any context. In this paper, we focus our attention on MILPs in the specific framework of Data Envelopment Analysis (DEA), where the determination of a score of technical efficiency of a set of Decision Making Units (DMUs) is one of the main objectives. In particular, we propose a new hyper-matheuristic grounded on a MILP-based decomposition in which the optimization problem is divided into two hierarchical subproblems. The new approach decomposes the model into discrete and continuous variables, treating each subproblem through different optimization methods. In particular, metaheuristics are used for dealing with the discrete variables, whereas exact methods are used for the set of continuous variables. The metaheuristics use an indirect representation that encodes an incomplete solution for the problem, whereas the exact method is applied to decode the solution and generate a complete solution. The experimental results, based on simulated data in the context of Data Envelopment Analysis, show that the solutions obtained through the new approach outperform those found by solving the problem globally using a metaheuristic method. Finally, regarding the new hyper-matheuristic scheme, the best algorithm selection is found for a set of cooperative metaheuristics ans exact optimization algorithms.

Inspired by the recent success of parallelized exact methods to solve difficult scheduling problems, we present preliminary results of a general parallel beam search framework for combinatorial optimization problems. Beam search is a constructive metaheuristic traversing a search tree layer by layer while keeping in each layer a bounded number of promising nodes to consider many partial solutions in parallel. We propose a variant which is suitable for intra-node parallelization by multithreading with data parallelism. For sufficiently large problem instances and beam widths our work-in-progress implementation in the JIT-compiled Julia language admits promising speed-ups over 30x on 32 cores with uniform memory access for the Permutation Flow Shop Scheduling (PFSP) problem with flowtime objective.

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.

The usage of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase of interest in the past few years. UAVs find applications where human action would be ineffective, slow, risky or even impossible. With a three-dimensional mobility and payload flexibility, they can indeed be used for missions like infrastructure inspection or search and rescue. Most applications have considered the usage of a single UAV so far, but using several autonomous UAVs as a swarm would overcome some drawbacks like mission duration (if one UAV of the swarm is out of battery, the latter can pause the mission while others keep flying) or payload capacity (the payload can be distributed among all UAVs). Designing an efficient swarm of UAVs however comes with some challenges, including the difficulty to define the necessary distributed algorithms to tackle specific tasks. The desired global behaviour, e.g. monitoring an area or transporting material, is indeed emergent from local interactions. Manually designing these local interactions can therefore be tedious and time-consuming. This work thus aims at automating that process in the context of area coverage. The first step has been to define a multi-objective optimisation problem to represent an area coverage mission. The second step has been to define an algorithm to generate distributed heuristic for the latter optimisation problem. The proposed method is based on Q-learning and experimental results demonstrate that it permits to generate heuristics that not only outperform the state-of-the-art, but also provide a high stability.

In recent years, research in applying optimization approaches in the automatic design of deep neural networks has become increasingly popular. Although various approaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this hot research topic. In this article, we propose a unified way to describe the various optimization algorithms that focus on common and important search components of optimization algorithms: representation, objective function, constraints, initial solution(s), and variation operators. In addition to large-scale search space, the problem is characterized by its variable mixed design space, it is very expensive, and it has multiple blackbox objective functions. Hence, this unified methodology has been extended to advanced optimization approaches, such as surrogate-based, multi-objective, and parallel optimization.

Our aim in this study is to propose a novel approach for optimizing a variant of the home health care routing problem, in which the health-care workers perform a requested number of visits to patients by using electric vehicles. Our approach on the one hand uses multi-objective optimization methods to find optimal planning routes of EVs and on the other hand deals with uncertainty in some major problem constraints. The goal is to help decision makers choosing appropriate routes; while minimising the cost of electric vehicular displacements in terms of: the total travel cost for all home-health workers and the waiting time for all patients. Moreover, we consider in our problem the fuzzy nature of some major constraints.

Bayesian Optimization using Gaussian Processes is a popular approach to deal with optimization involving expensive black-box functions. However, because of the assumption on the stationarity of the covariance function defined in classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the optimization problem. To overcome this issue, Deep Gaussian Processes can be used as surrogate models instead of classic Gaussian Processes. This modeling technique increases the power of representation to capture the non-stationarity by considering a functional composition of stationary Gaussian Processes, providing a multiple layer structure. This paper investigates the application of Deep Gaussian Processes within Bayesian Optimization context. The specificities of this optimization method are discussed and highlighted with academic test cases. The performance of Bayesian Optimization with Deep Gaussian Processes is assessed on analytical test cases and aerospace design optimization problems and compared to the state-of-the-art stationary and non-stationary Bayesian Optimization approaches.

International audience

Modern algorithms for symmetric and asymmetric encryptions are not suitable to provide security of data that needs data processing. They cannot perform calculations over encrypted data without first decrypting it when risks are high. Residue Number System (RNS) as a homomorphic encryption allows ensuring the confidentiality of the stored information and performing calculations over encrypted data without preliminary decoding but with unacceptable time and resource consumption. An important operation for encrypted data processing is a number comparison. In RNS, it consists of two steps: the computation of the positional characteristic of the number in RNS representation and comparison of its positional characteristics in the positional number system. In this paper, we propose a new efficient method to compute the positional characteristic based on the approximate method. The approximate method as a tool to compare numbers does not require resource-consuming non-modular operations that are replaced by fast bit right shift operations and taking the least significant bits. We prove that in case when the dynamic range of RNS is an odd number, the size of the operands is reduced by the size of the module. If one of the RNS moduli is a power of two, then the size of the operands is less than the dynamic range. We simulate proposed method in the ISE Design Suite environment on the FPGA Xilinx Spartan-6 SP605 and show that it gains 31% in time and 37% in the area on average with respect to the known approximate method. It makes our method efficient for hardware implementation of cryptographic primitives constructed over a prime finite field.

International audience; This paper introduces a bi-objective winner determination problem which is based on English auctions. Most models of combinatorial auctions (winner determination problem) do not allow the bidder to update his offer, due to the fact that these mechanisms are static. However in reality bidders are in rough competition while there is time for auction. In this work we give a mathematical formulation of the dynamic model of the bi-objective winner determination problem, where the objectives are: (i) maximization of the total income, (ii) maximization of the number of items sold. This problem is based on the English auction mechanism, which allows bidders to renew their bids until the end of the exercise period. Then the solution is proposed by giving an algorithm based on an hybridization of a metaheuristic with a fuzzy dominance relation. A numerical experimentation using this algorithm on simulated data gives rise to satisfactory results.

International audience; Mixed Integer Linear Programs (MILPs) are usually NP-hard mathematical programming problems, which present difficulties to obtain optimal solutions in a reasonable time for large scale models. Nowadays, metaheuristics are one of the potential tools for solving this type of problems in any context. In this paper, we focus our attention on MILPs in the specific framework of Data Envelopment Analysis (DEA), where the determination of a score of technical efficiency of a set of Decision Making Units (DMUs) is one of the main objectives. In particular, we propose a new hyper-matheuristic grounded on a MILP-based decomposition in which the optimization problem is divided into two hierarchical subproblems. The new approach decomposes the model into discrete and continuous variables, treating each subproblem through different optimization methods. In particular, metaheuristics are used for dealing with the discrete variables, whereas exact methods are used for the set of continuous variables. The metaheuristics use an indirect representation that encodes an incomplete solution for the problem, whereas the exact method is applied to decode the solution and generate a complete solution. The experimental results, based on simulated data in the context of Data Envelopment Analysis, show that the solutions obtained through the new approach outperform those found by solving the problem globally using a metaheuristic method. Finally, regarding the new hyper-matheuristic scheme, the best algorithm selection is found for a set of cooperative metaheuristics ans exact optimization algorithms.

Correction to: S. Greco et al. (Eds.): Metaheuristics for Combinatorial Optimization, AISC 1332, https://doi.org/10.1007/978-3-030-68520-1 The book was inadvertently published with the incorrect volume number (1336) and this has been corrected now (1332).

The usage of Unmanned Aerial Vehicles (UAVs) is gradually gaining momentum for commercial applications. These however often rely on a single UAV, which comes with constraints such as its range of capacity or the number of sensors it can carry. Using several UAVs as a swarm makes it possible to overcome these limitations. Many metaheuristics have been designed to optimise the behaviour of UAV swarms. Manually designing such algorithms can however be very time-consuming and error prone since swarming relies on an emergent behaviour which can be hard to predict from local interactions. As a solution, this work proposes to automate the design of UAV swarming behaviours thanks to a Q-learning based hyper heuristic. Experimental results demonstrate that it is possible to obtain efficient swarming heuristics independently of the problem size, thus allowing a fast training on small instances.

Simulated annealing (SA) is a well-known stochastic local search algorithm for solving unconstrained optimization problems. It mimics the annealing process used in the metallurgy to approximate the global optimum of an optimization problem and uses the temperature to control the search. Unfortunately, the effectiveness of simulated annealing drops drastically when dealing with a large-scale optimization problem. This is due in general to a premature convergence or a stagnation. The both phenomenons can be avoided by a good balance between exploitation and exploration. This paper focuses on the same problem encountered by simulated annealing and try to heal it using the quaternion which are a number system that extends complex numbers. Quaternion representation helps the simulated annealing algorithm to smooth the fitness landscape and thus avoiding to get stuck in the local optima by expanding the original search space. Empirical analysis was conducted on many numerical benchmark functions. The experimental results show that the quaternion representation of neighborhood improves the solution quality compared with the classical simulated annealing. Our approach was also compared with other nature-inspired optimization algorithms. It was shown that the quaternion simulated annealing overcomes other heuristics in terms of solution quality for most of the benchmark functions.

Particle swarm optimization (PSO) is a population-based stochastic metaheuristic algorithm; it has been successful in dealing with a multitude of optimization problems. Many PSO variants have been created to boost its optimization capabilities, in particular, to cope with more complex problems. In this paper, we provide a new approach of multi-population particle swarm optimization with a cooperation strategy. The proposed algorithm splits the PSO population into four sub swarms and attributes a main role to each one. A machine learning technique is designed as an individual level to allow each particle to determine its suitable swarm membership at each iteration. In a collective level, cooperative rules are designed between swarms to ensure more diversity and realize the better solution using a Master/Slave cooperation scheme. Several simulations are performed on a set of benchmark functions to examine the performances of this approach compared to a multitude of state of the art of PSO variants. Experiments reveal a good computational efficiency of the presented method with distinguishable performances.

The usage of Unmanned Aerial Vehicles (UAVs) is gradually gaining momentum for commercial applications. The vast majority considers a single UAV, which comes with several constraints such as its range of operations or the number of sensors it can carry. Using multiple autonomous UAVs simultaneously as a swarm makes it possible to overcome these limitations. However, manually designing complex emerging behaviours like swarming is a difficult and tedious task especially for such distributed systems which performance is hardly predictable. This article therefore proposes to automate the design of UAV swarming behaviours by defining a multi-objective optimisation problem, so called Coverage of a Connected-UAV Swarm (CCUS), and designing a Q-Learning based Hyper-Heuristic (QLHH) for generating distributed CCUS heuristics. Experimental results demonstrate the capacity of QLHH to generate efficient heuristics for any instance from a given class.

Within the context of Kriging model-based optimization, an efficient way called infill sampling criteria is used to choose the best additional update points to refine the Kriging model and gradually predict the optimum value at each iteration. Most of these criteria use one criterion or combine various criteria into a single numerical formula. However, this idea does not simultaneously ensure the local exploitation of the model and the exploration of the search space. In this paper, we introduce an optimization strategy based on Kriging surrogate model and a multi-objective infill sampling (MIS) strategy that considers simultaneously the predicted mean (local exploitation) and the approximation uncertainty (global exploration) as two conflicting criteria. Internally, an evolutionary algorithm called NSGA-III is used to optimize this criterion. Numerical experimentations are performed on a large set of analytical benchmark to highlight the robustness and efficiency of the presented strategy.

While the sector of unmanned aerial vehicles (UAVs) is experiencing an exponential growth since several years, the majority of applications consider single devices which come with limitations such as flight duration or payload capacity. A promising way to overcome these is the usage of multiple autonomous UAVs synergistically, also referred to as swarms. Many metaheuristics have been manually designed to optimise the performance of swarms of unmanned vehicles. However developing and fine tuning efficient collective behaviours can be a challenging and time-consuming task. This article proposes to automate the generation of UAV swarming behaviours which optimise the Coverage of a Connected UAV Swarm (CCUS) problem where both the coverage time and the network connectivity are considered. To this end, we introduce a novel generative hyper-heuristic based on Q-Learning (QLHH) and evaluate the performance of the heuristics it generates to manually designed heuristics using state-of-the-art coverage and connectivity metrics. The obtained results demonstrate the capacity of QLHH to generate efficient distributed heuristics for the CCUS optimisation problem.

One of the most significant challenges facing optimization models for the demand-side management (DSM) is obtaining feasible solutions in a shorter time. In this paper, the DSM is formulated in a smart building as a linear constrained multi-objective optimization model to schedule both electrical and thermal loads over one day. Two objectives are considered, energy cost and discomfort caused by allowing flexibility of loads within an acceptable comfort range. To solve this problem, an integrative matheuristic is proposed by combining a multi-objective evolutionary algorithm as a master level with an exact solver as a slave level. To cope with the non-triviality of feasible solutions representation and NP-hardness of our optimization model, in this approach discrete decision variables are encoded as partial chromosomes and the continuous decision variables are determined optimally by an exact solver. This matheuristic is relevant for dealing with the constraints of our optimization model. To validate the performance of our approach, a number of simulations are performed and compared with the goal programming under various scenarios of cold and hot weather conditions. It turns out that our approach outperforms the goal programming with respect to some comparison metrics including the hypervolume difference, epsilon indicator, number of the Pareto solutions found, and computational time metrics.

International audience

Having many non dominated solutions in bi-objective optimization problems, this paper aims to cluster the Pareto front using Euclidean distances. The p-center problems, both in the discrete and continuous versions, become solvable with a dynamic programming algorithm. Having N points, the complexity of clustering is \(O(KN\log N)\) (resp. \(O(KN\log ^2 N)\)) time and O(N) memory space for the continuous (resp. discrete) K-center problem for \(K\geqslant 3\), and in \(O(N\log N)\) time for such 2-center problems. Furthermore, parallel implementations allow quasi-linear speed-up for the practical applications.

International audience; In this paper we address the problem of computing suitable representations of the set of approximate solutions of a given multi-objective optimization problem via stochastic search algorithms. For this, we will propose different archiving strategies for the selection of the candidate solutions maintained by the generation process of the stochastic search process, and investigate them further on analytically and empirically. For all archivers we will provide upper bounds on the approximation quality as well as on the cardinality of the limit solution set. We conclude this work by a comparative study on some test problems in order to visualize the effect of all novel archiving strategies.

Improving performance and reducing costs are major challenges in many engineering design problems. The processes or accurate models are usually time-consuming and computationally expensive; therefore, the objective function requires a large number of evaluations. This paper considers surrogate modeling to approximate the expensive function and to ensure the quality of results in a reduced CPU time for mono-objective optimization. The Basic idea of surrogate models, also known as a meta-model, is to build a model from a sampled data, then, the outputs of other design data can be predicted by the approximated model instead of using the heavy one. Once the surrogate model is built, an optimization method is used to look for new design points until convergence. In this work, we propose a surrogate-based optimization algorithm using backtracking search algorithm optimization, and the thin spline basis function to build the surrogate model. During the construction of the surrogate, a minimization problem of error is carried out by updating the position of the node that produces the maximum error. Experiments are carried out on many test functions.

Cloud storages delivered as services are made available to the general public. As cloud storage provider prices become low, they have moved into the mainstream of storage technology. However, there are various factors that cause many potential users do not use it intensively. There exist high risks for confidentiality, integrity, and availability violation associated with the loss of information, denial of access, technical failures, etc. In this article, we propose a two-level secret sharing scheme (TL-SSS) based on a residue number system (RNS) for a configurable, reliable, and secure distributed data storage. RNS moduli of a special type increase the reliability of the data storage system and reduce the computational complexity of the data encoding and decoding from linear-logarithmic to linear. TL-SSS is the weighted data access structure. It creates and distributes data shares according to the characteristics of the cloud storages under various scenarios. We provide a solution that improves system reliability without reduction of the security level. In contrast to classical solutions, it can restore the data with less available shares than the state-of-the-art approaches.

In this paper, we solve a discrete bilevel problem with multiple objectives at the lower level and constraints at the upper level coupling variables of both levels. In the case of a multiobjective lower level, we deal with a set of Pareto‐efficient solutions rather than a single optimal lower level solution. To calculate the upper level objective function value, we need to select one solution out of a potentially large set of efficient lower level solutions. To avoid the enumeration of the whole set of Pareto solutions, we formulate an auxiliary mixed integer linear programming problem with a large number of constraints. We propose an iterative exact method to solve it. To find a near‐optimal upper level solution, we apply a metaheuristic. The method is tested on the discrete (r|p)‐centroid problem with multiple objectives at the lower level.

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 robustness approach to deal with fuzziness in the multi-objective context. The new approach tries to achieve robust routes that minimize two fuzzy-valued objectives, the total traveled distance and total tardiness time. Additionally, we suggest to refine our previously proposed algorithms for integrating robustness. An experimental study based on a Monte-Carlo simulation is finally carried out to evaluate the obtained results.

International audience; 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 this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.

Planning the maintenance of nuclear power plants is a complex optimization problem, involving a joint optimization of maintenance dates, fuel constraints and power production decisions. This paper investigates Mixed Integer Linear Programming (MILP) matheuristics for this problem, to tackle large size instances used in operations with a time scope of five years, and few restrictions with time window constraints for the latest maintenance operations. Several constructive matheuristics and a Variable Neighborhood Descent local search are designed. The matheuristics are shown to be accurately effective for medium and large size instances. The matheuristics give also results on the design of MILP formulations and neighborhoods for the problem. Contributions for the operational applications are also discussed. It is shown that the restriction of time windows, which was used to ease computations, induces large over-costs and that this restriction is not required anymore with the capabilities of matheuristics or local search to solve such size of instances. Our matheuristics can be extended to a bi-objective optimization extension with stability costs, for the monthly re-optimization of the maintenance planning in the real-life application.

Graphical abstractDisplay Omitted HighlightsA new multiobjective modeling for the biclustering problem.A new hybrid multiobjective algorithm gradually conceived for best results.Extracting relevant biclusters with large sizes compared to classical methods. Microarray data analysis is a challenging problem in the data mining field. Actually, it represents the expression levels of thousands of genes under several conditions. The analysis of this data consists on discovering genes that share similar expression patterns across a sub-set of conditions. In fact, the extracted informations are submatrices of the microarray data that satisfy a coherence constraint. These submatrices are called biclusters, while the process of extracting them is called biclustering.Since its first application to the analysis of microarray 1], many modeling and algorithms have been proposed to solve it. In this work, we propose a new multiobjective model and a new metaheuristic HMOBIibea for the biclustering problem. Results of the proposed method are compared to those of other existing algorithms and the biological relevance of the extracted information is validated. The experimental results show that our method extracts very relevant biclusters, with large sizes with respect to existing methods.

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 the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems.

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 system that holds a large amount of application programs and data.

International audience; 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 problems, permutation flowshop scheduling problem (PFSP) with the objectives of minimizing makespan and the total flowtime of jobs. The proposed ILS based hyper-heuristic proved its general and applicable across the studied problems.

In an ideal world, it would be straightforward to identify the most suitable optimisation method to use in the solution of a given optimisation problem. However, although some methods may be more widely applicable than others, it is impossible a priori to know which method will work best. This may be due to the particular mathematical properties of the mathematical model, i.e. the formulation. It may also be due to the representation of the variables in the model. This combination of choices of method, representation and formulation makes it difficult to predict which combination may be best.

The management of emergency situations is considered as a complex field due to its confusing situations as well as its difficulty in anticipating the behavior of evacuees while dealing with such disasters. Therefore, it is necessary to establish an efficient evacuation plan that should simulate real-life evacuation process. This paper defines the earliest arrival flow problem with load-dependent travel time (EAFP-LDT) as the more adequate macroscopic model for evacuation plan. We maintain the load-dependent travel time constraint since it defines the dependence between the crowd's rate and the travel time in doors. To solve this problem, we propose a parallel ant colony optimization algorithm (PACO) to deal with the emergency evacuation guidance problem in buildings based on EAFP-LDT. This proposed algorithm provides a realistic and an efficient evacuation plan which permits evacuees to follow the shortest path with less crowd.

Data Envelopment Analysis (DEA) is a nonparametric methodology for estimating technical efficiency of a set of Decision Making Units (DMUs) from a dataset of inputs and outputs. This paper is devoted to computational aspects of DEA models under the application of the Principle of Least Action. This principle guarantees that the efficient closest targets are determined as benchmarks for each assessed unit. Usually, these models have been addressed in the literature by applying unsatisfactory techniques, based fundamentally on combinatorial NPhard problems. Recently, some heuristics have been developed to partially solve these DEA models. This paper improves the heuristic methods used in previous works by applying a combination of metaheuristics and an exact method. Also, a parameterized scheme of metaheuristics is developed in order to implement metaheuristics and hybridations/combinations, adapting them to the particular problem proposed here. In this scheme, some parameters are used to study several types of metaheuristics, like Greedy Random Adaptative Search Procedure, Genetic Algorithms or Scatter Search. The exact method is included inside the metaheuristic to solve the particular model presented in this paper. A hyperheuristic is used on top of the parameterized scheme in order to search, in the space of metaheuristics, for metaheuristics that provide solutions close to the optimum. The method is competitive with exact methods, obtaining fitness close to the optimum with low computational time J. Aparicio and M. González thank the financial support from the Spanish ‘Ministerio de Economa, Industria y Competitividad’ (MINECO), the ‘Agencia Estatal de Investigacion’ and the ‘Fondo Europeo de Desarrollo Regional’ under grant MTM2016-79765-P (AEI/FEDER, UE). Additionally, D. Giméenez thanks the financial support from the Spanish MINECO, as well as by European Commission FEDER funds, under grant TIN2015-66972-C5-3-R.

Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. It is a critical step in all image analysis tasks in which the final information is gained from the combination of various data sources like in image fusion or change detection.

International audience; This book highlights recent research on metaheuristics for biomedical engineering, addressing both theoretical and applications aspects. Given the multidisciplinary nature of bio-medical image analysis, it has now become one of the most central topics in computer science, computer engineering and electrical and electronic engineering, and attracted the interest of many researchers.To deal with these problems, many traditional and recent methods, algorithms and techniques have been proposed. Among them, metaheuristics is the most common choice. This book provides essential content for senior and young researchers interested in methodologies for implementing metaheuristics to help solve biomedical engineering problems.

International audience; 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 of optimal solutions based on the preferences of the decision maker. Commonly, interactive methods from the literature consider the user preferences at every iteration by means of weight vectors or reference points. Besides the fact that mathematical programming techniques only produce one solution at each iteration, they generally require first or second derivative information, that limits its applicability to certain problems. The approach proposed in this paper allows to steer the search into any direction in the objective space for optimization problems of discrete nature. This provides a more intuitive way to set the preferences, which represents a useful tool to explore the regions of interest of the decision maker. Numerical results on multi-objective multi-dimensional knapsack problem instances show the interest of the proposed approach.

International audience; 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 energy. In this paper, we present an Energy-aware Multi-start Local Search algorithm (EMLS-ONC) that optimizes the energy consumption of an OpenNebula based Cloud. Moreover, we propose a Pareto Multi-Objective version of the EMLS-ONC called EMLS-ONC-MO dealing with both the energy consumption and the Service Level Agreement (SLA). The objective is to find a Pareto tradeoff between reducing the energy consumption of the cloud while preserving the performance of Virtual Machines (VMs). The different schedulers have been experimented using different arrival scenarios of VMs and different hardware configurations (artificial and real). The results show that EMLS-ONC and EMLS-ONC-MO outperform the other energy- and performance-aware algorithms in addition to the one provided in OpenNebula by a significant margin on the considered criteria. Besides, EMLS-ONC and EMLS-ONC-MO are proved to be able to assign at least as many VMs as the other algorithms.

International audience; 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 bins. The second one is the minimization of the maximum length of a bin. The third objective consists in balancing the load overall the bins by minimizing the difference between the maximum length and the minimum length of a bin. Two population-based metaheuristics are performed to tackle this problem. These metaheuristics use different indirect encoding approaches in order to find good permutations of items which are then packed by a separate decoder routine whose parameters are embedded in the solution encoding. It leads to a self-adaptive metaheuristic where the parameters are adjusted during the search process. The performance of these strategies is assessed and compared against benchmarks inspired from the literature.

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'' constraint. The objective is to find a trade-off between the number of bins and the maximum height of a bin. This problem has various real-world applications (computer science, production planning and logistics). Based on the special structure of its Pareto front, we propose two iterative resolution approaches for solving the Mo2-DBPP. In each approach, we use several lower bounds, heuristics and metaheuristics. Computational experiments are performed on benchmarks inspired from the literature to compare the effectiveness of the two approaches.

International audience; 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-grained decomposition, a conceptual model is proposed and is validated by regarding a number of state-of-the-art methodologies as simple variants of the same structure. This model is then incorporated into the ParadisEO-MO software framework. This framework has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.