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The IEEE Workshop on Parallel / Distributed Combinatorics and Optimization aims at providing a forum for scientific researchers and engineers on recent advances in the field of parallel or distributed computing for difficult combinatorial optimization problems, like 0–1 multidimensional knapsack problems, cutting stock problems, scheduling problems, large scale linear programming problems, nonlinear optimization problems and global optimization problems. Emphasis is placed on new techniques for the solution of these difficult problems like cooperative methods for integer programming problems. Techniques based on metaheuristics and nature-inspired paradigms are considered. Aspects related to Combinatorial Scientific Computing (CSC) are considered. In particular, we solicit submissions of original manuscripts on sparse matrix computations, graph algorithm and original parallel or distributed algorithms. The use of new approaches in parallel and distributed computing like GPU, MIC, FPGA, volunteer computing are considered. Application to cloud computing, planning, logistics, manufacturing, finance, telecommunications and computational biology are considered.

In this chapter, an analytical parameter tuning for the Archive Multi-Objective Simulated Annealing (AMOSA) with a fuzzy logic controller is proposed. The analytical tuning is used to compute the initial and final temperature, as well as the maximum metropolis length. The fuzzy logic controller is used to adjust the metropolis length for each temperature. These algorithms are used to solve the Heterogeneous Computing Scheduling Problem. The tuned AMOSA with a fuzzy logic controller is compared against an AMOSA without tuning. Three quality indicators are used to compare the performance of the algorithms, these quality indicators are hypervolume, generational distance, and generalized spread. The experimental results show that the tuned AMOSA with fuzzy logic controller achieves the best performance.

Plug-in hybrid (PH) buses offer range and operating flexibility of buses with conventional internal combustion engines with environmental. However, when they are frequently charged, they also enable societal benefits (emissions- and noise-related) associated with electric buses. Thanks to geofencing, pure electric drive of PH buses can be assigned to specific locations via a back-office system. As a result, PH buses not only can fulfil zero-emission (ZE) zones set by city authorities, but they can also minimize total energy use thanks to selection of locations favouring (from energy perspective) electric drive. Such a location-controlled behaviour allows executing targeted air quality improvement and noise reduction strategies as well reducing energy consumption. However, current ZE zone assignment strategies used by PH buses are static—they are based on the first-come-first serve rule and do not consider traffic conditions. In this article, we propose a novel recommendation system, based on artificial intelligence, that allows PH buses operating efficiently in a dynamic environment, making the best use of the available resources so that emission- and noise-pollution levels are minimized.

Several previous works have shown how using prior knowledge within machine learning models helps to overcome the curse of dimensionality issue in high dimensional settings. However, most of these works are based on simple linear models (or variations) or do make the assumption of knowing a pre-defined variable grouping structure in advance, something that will not always be possible. This paper presents a hybrid genetic algorithm and machine learning approach which aims to learn variables grouping structure during the model estimation process, thus taking advantage of the benefits introduced by models based on problem-specific information but with no requirement of having a priory any information about variables structure. This approach has been tested on four synthetic datasets and its performance has been compared against two well-known reference models (LASSO and Group-LASSO). The results of the analysis showed how that the proposed approach, called GAGL, considerably outperformed LASSO and performed as well as Group-LASSO in high dimensional settings, with the added benefit of learning the variables grouping structure from data instead of requiring this information a priory before estimating the model.

Dynamic optimization problems have attracted the attention of researchers due to their wide variety of challenges and their suitability for real-world problems. The application of hyper-heuristics to solve optimization problems is another area that has gained interest recently. These algorithms can apply a search space exploration method at different stages of the execution for finding high quality solutions. However, most of the proposed works using these methodologies do not focus on the development of hyper-heuristics for dynamic optimization problems. Despite that, they arise as very appropriate methods for dynamic problems, being highly responsive and able to quickly adapt to any possible changes in the problem environment. In this paper, we present a brief study of the most salient previously proposed hyper-heuristics to solve dynamic optimization problems, and classify them, taking into consideration the complexity of their low-level heuristics. Then, we identify some the most important research areas that have been vaguely explored in the Literature yet.

Today's IT systems and IT processes must be ready to handle change in an efficient and responsive manner to allow businesses to both evolve and adapt to a changing world. In this paper we describe an approach that consists of using simulation based multi-objective optimization to select optimal ITIL change management process strategies that help IT managers achieve process efficiency as a Critical Success Factor (CSF). A multi-method simulation model, which is based on agent-based and discrete-event simulation paradigms, has been built to simulate the whole process lifecycle, since the change initiation until its closure. As most engineering problems, assuring an efficient delivery of the change management process requires optimizing simultaneously the corresponding Key Performance Indicators (KPIs) in which the process-efficiency CSF can be rolled down. In this paper, we show the results of applying two well-known Multi-Objective Evolutionary Algorithms, namely NSGA-II and SPEA2, to obtain a set of optimal solutions for the KPIs associated with delivering process efficiency as a CSF. We also compare the results obtained with the output from the single-objective optimization algorithm provided by the simulation tool. The experimental work included shows how the approach can provide the IT manager with a wide range of high quality solutions to support them in their decision-making towards CSF achievement.

We present a novel method to automatically generate new parallel solvers for optimization problems, called the Virtual Savant. It applies machine learning to model a reference algorithm (which is treated as a black box) from its solutions to a given problem, and after, it is able to efficiently and accurately reproduce its solutions on new unseen problem instances. Additionally, the generated parallel algorithm is scalable to a different problem dimension. We analyze the performance and accuracy of our novel technique on a scheduling problem, and we use instances of different features and sizes. Virtual Savant was used to learn from an exact algorithm, a well-known heuristic, and a specialized parallel metaheuristic. During our experiments, our method solved to optimality up to 95.5% of the 200 unseen instances tested. For larger instances, it is not only highly competitive with the reference algorithms, but it is even able to outperform them in some cases. Another outstanding result is that Virtual Savant improved its accuracy when scaling the problem size (without additional training), with respect to the original algorithm.

We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first multi-objective cooperative coevolutionary algorithm based on PSO in the literature. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. In order to demonstrate the effectiveness of CCSMPSO, we compare our work with the original SMPSO and three different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II, CCSPEA2 and CCMOCell, along with their original sequential counterparts. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better or comparable results in terms of solution quality, when evaluated against three other CC algorithms and four state-of-the-art optimizers (namely SMPSO, NSGA-II, SPEA2, and MOCell), respectively. We then provide a scalability analysis, which consists of two studies. First, we analyze how the algorithms scale when varying the problem size, i.e., the number of variables. Second, we analyze their scalability in terms of parallelization, i.e., the impact of using more computational cores on the quality of solutions and on the execution time of the algorithms. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front.

This article presents how Virtual Savant (VS) can be used to automatically learn, from an exact algorithm, how to solve the basic independent Next Release Problem in a quick and accurate way. This variant of the Next Release Problem (NRP) is in essence a 0/1 Knapsack Problem and VS is applied to solve the underlying optimization problem. VS is a generic problem-solving approach based on machine learning and heuristics, that works by mimicking how a reference program produces solutions to problem instances. Essentially, VS learns how to generate solutions to a given problem from a reference algorithm and a training set of instances, and it is able to efficiently solve new problem instances by using the acquired knowledge. In this paper, an exact optimizer is used as a reference algorithm. Hence, we are using VS to learn from optimal solutions, which helps to reduce the approximation error inherent to the learning process. We compare five versions of VS (differing in the heuristics they implement) on a large benchmark, composed of problems with different sizes and difficulties. For the best VS configuration, which also has the lowest computational complexity, computed solutions differ less than 1% from the optima in the worst case. Therefore, VS succeeds in learning how to solve the problem under study, and it does so in a highly efficient way, exempting the programmer from having a deep knowledge of the problem domain or highly specialized parallel programming skills.

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems. First published online 12 July 2019

This work addresses the multi-objective resource provisioning problem for building cloud-based CDNs. The optimization objectives are the minimization of VM, network and storage cost, and the maximization of the QoS for the end-user. A brokering model is proposed such that a single cloud-based CDN is able to host multiple content providers applying a resource sharing strategy. Following this model, an offline multiobjective evolutionary approach is applied to optimize resource provisioning while a greedy heuristic is proposed for addressing online routing of content. Experimental results indicate the proposed approach may reduce total costs by up to 10.6% while maintaining high QoS values.

High-Performance Computing (HPC) is a growing necessity of our technological society, HPC demands high loads of parallel computing jobs, an optimal scheduling of the parallel applications tasks is a priority to meet the demands of its users on time. Branch-and-bound (BB) Algorithms and Mathematical Programming (MP) solve complex optimization problems in an optimal manner, some MP or BB even have parallel computing capabilities, making them suitable solutions to solve real-world problems. In this paper, we propose two exact algorithms, a BB and a MP Model for scheduling precedence-constrained applications, on heterogeneous computing systems, as far as we known the first ones on his kind presented in the state of the art. One major contribution of the work is the proposed formulations of the objective function in both methods. Experimental results obtained more than twenty optimal values for synthetic applications from the literature.

We propose a new accurate Micro Genetic Algorithm ( μ GA) for multi-objective optimization problems that we call Micro-FAME (or μ FAME). The distinctive feature of μ FAME with respect to the other existing multi-objective algorithms in the literature is its high elitism and fast convergence, produced by the application of the evolution directly on the Pareto front approximation. This means that it has a bounded population of variable size, determined by the number of non-dominated solutions found so far. In μ FAME (and generally in μ GAs) the premature convergence problem acquires especial relevance, and generating and maintaining solutions diversity is of extreme importance to deal with it. This is especially significant in the multi-objective field, where the algorithm looks for a wide and diverse set of non-dominated solutions. In μ FAME, the diversity loss that high elitism usually produces is overcome by the decisions driven by the Fuzzy Inference System (FIS), that chooses the most appropriate operators to apply during the evolution to promote both diversity and accuracy of solutions. The algorithm arises as a suitable solution for problems with a computationally heavy fitness function, because it is able to quickly achieve good quality local optimal solutions, but it is also highly competitive in long runs. Experiments reveal a great performance of μ FAME according to several convergence related metrics, when comparing against seven state-of-the-art algorithms on a large set of benchmark problems, as well as on a real-world engineering one with an expensive fitness function.

This work presents a novel parallel branch and bound algorithm to efficiently solve to optimality a set of instances of the multi-objective flexible job shop scheduling problem for the first time, to the very best of our knowledge. It makes use of the well-known NSGA-II algorithm to initialize its upper bound. The algorithm is implemented for shared-memory architectures, and among its main features, it incorporates a grid representation of the solution space, and a concurrent priority queue to store and dispatch the pending sub-problems to be solved. We report the optimal Pareto front of thirteen well-known instances from the literature, which were unknown before. They will be very useful for the scientific community to provide more accuracy in the performance measurement of their algorithms. Indeed, we carefully analyze the performance of NSGA-II on these instances, comparing the results against the optimal ones computed in this work. Extensive computational experiments show that the proposed algorithm using 24 cores achieves a speedup of 15.64x with an efficiency of 65.20%.

We present in this work the first parallel implementation of Virtual Savant (VS), a novel optimization method that is able to quickly generate pseudo-optimal solutions to a given combinatorial problem, thanks to its parallel pattern recognition engine. The proposed parallel implementation does not require any information exchange between the threads during the run, they just get/send the required information before/after the execution. This design allows for a flexible algorithm that can perform efficiently on both shared- and distributed-memory systems. Our implementation uses both OpenMP for parallel architectures and MPI for distributed environments, which can efficiently make use of both kind of systems. The performance of VS is extensively analyzed on four different computing infrastructures, varying the number of threads used on each considered architecture. In addition, we propose a simulator to accurately predict the performance of VS on any parallel system. Experimental results show that VS is able to make an efficient use of the available computing resources, showing good scalability properties on all studied architectures.

Engineering students often find difficulties in understanding electrical diagrams, specially those from Design and Mechanical Engineering. A tool that can automatically generate the ladder diagram out of the wiring diagram would be extremely useful for them in their studies. Unfortunately, such a tool does not exist yet. We present in this paper a novel CAD tool that offers this functionality. The tool, called CADDI (standing for CAD for Electrical Diagrams) is designed to help students better understanding wiring and ladder diagrams. In CADDI, the user can either draw the ladder diagram using its graphical interface, or code the wiring diagram using a novel simple programming language, and it will automatically generate its ladder diagram. The mentioned new language was especially designed for this purpose by the students of Automata Theory and Formal Languages subject, from Computer Science Engineering degree of the University of Cadiz. In order to make CADDI accessible to all students, it was implemented as a web page. The tool has become a great assistance for students, who regularly use it during their studies.

Literature reveals that optimization algorithms are generally composed of a large number of parameters that highly influence on its performance. In the early stages of the definition of a new algorithm, it is crucial to know how the uncertainty in the input parameters affects the behavior of the algorithm, influencing on its final output, so that it is possible to set up the most efficient configuration.

MANETs are specially vulnerable against security attacks. For protecting them, security solutions are traditionally addressed by the so-called intrusion detection and response systems. Nevertheless, using reactive (response) solutions, once the attack is detected, rely in long attack mitigation times. In this work, we propose the use of relay node placement techniques as proactive security solutions. Preliminary experimentation demonstrates the suitability and feasibility of such approaches, not only in reducing the attack mitigation time but in replacing detection and response based traditional solutions.

Sensitivity analysis is a mathematical tool that distributes the uncertainty of the output of a model among its different input variables. We use in this work the Extended Fourier Amplitude Sensitivity Test to carefully analyze the impact of 54 LLVM code optimization operators on the execution time of nine benchmark software programs. Experiments presented involve performing over 16 million executions. The results show that the different LLVM transformations have a low direct effect on the execution time, but it becomes meaningful when considering the transformation in combination with the others (almost 60% average impact by all passes on all considered benchmarks). These results provide slight indications on the transformations to apply for optimizing the software, revealing the extreme difficulty of the problem.

Support vector machines are widely used for classification and regression tasks. However, sequential implementations for support vector machines are usually unable to deal with the increasing size of current real-world learning problems. In this context, Intel®Xeon PhiTM processors allow easily incorporating high performance computing strategies to improve execution times. This article proposes a parallel implementation of the popular LIBSVM library, specially adapted to the Intel®Xeon PhiTM architecture. The proposed implementation is evaluated using publicly available datasets corresponding to classification and regression tasks. Results show that the proposed parallel version computes the same results than the original LIBSVM while reducing the time needed for training by up to a factor of 4.81.

This chapter analyzes the use of adaptive neighborhoods based on coalitions in evolutionary optimization frameworks. First, we introduce the concepts of evolutionary algorithms, population topologies and coalitions. We integrate all these topics to study how to avoid some of the drawbacks of previous evolutionary algorithms and to remove their typically required parameters. The main contribution of the chapter is a redefinition of the Evolutionary Algorithm with Coalitions (EACO), which uses cellular approaches with neighborhoods, allowing the formation of coalitions among cells as a way to create islands of evolution in order to preserve diversity. This idea speeds up the evolution of individuals grouped in high-quality coalitions that are quickly converging to promising solutions. In the results section, we successfully compare EACO with a canonical cGA (Cellular Genetic Algorithm), and provide evidences about the statistical significance of our results. We also analyze the influence of parameters in order to tune them up accordingly; and finally, we evaluate the performance of EACO under different complex network topologies.

This article introduces a new kind of broker for cloud computing, whose business relies on outsourcing virtual machines (VMs) to its customers. More specifically, the broker owns a number of reserved instances of different VMs from several cloud providers and offers them to its customers in an on-demand basis, at cheaper prices than those of the cloud providers. The essence of the business resides in the large difference in price between on-demand and reserved VMs. We define the Virtual Machine Planning Problem, an optimization problem to maximize the profit of the broker. We also propose a number of efficient smart heuristics (seven two-phase list scheduling heuristics and a reordering local search) to allocate a set of VM requests from customers into the available pre-booked ones, that maximize the broker earnings. We perform experimental evaluation to analyze the profit and quality of service metrics for the resulting planning, including a set of 400 problem instances that account for realistic workloads and scenarios using real data from cloud providers.

We propose a new accurate and fast memetic parallel optimization algorithm for the independent tasks scheduling problem. The new technique combines the Virtual Savant (VS) with a parallel genetic algorithm (called PA-CGA). VS is an optimization framework based on machine learning that learns from a reference set of (pseudo-)optimal solutions how to solve the problem, providing accurate results in extremely low run times. We propose in this work the use of VS to generate a highly accurate initial population for the PA-CGA. Results show how initializing the population with VS (we test two versions of VS, differing on its training process) significantly increases the accuracy of the PA-CGA, compared to two other population initialization techniques: random and using a state-of-the-art heuristic.

This chapter presents a new kind of cloud brokering model called virtual broker. The virtual broker owns and manages what we call a virtual cloud, composed by a set of reserved VMs from a number of public cloud providers. This new broker sublets its resources to its customers as on-demand VMs, at lower prices than those offered in the market. This model is feasible because of the large price difference between on-demand and reserved VMs in current pricing schemes. We propose a number of online and offline heuristics to efficiently manage the resources of the virtual broker in order to optimize its revenue, as well as the QoS level offered to the customers. Two realistic versions of the problem are proposed and analyzed. The results show the proposed brokering model is a profitable business model for both the virtual broker and the cloud customers, reducing cloud customer costs down to 80% when compared to traditional on-demand renting costs.

This paper investigates the automatic generation of a Map-Reduce program, which implements a heuristic for an NP-complete problem with machine learning. The objective is to automatically design a new concurrent algorithm that finds solutions of comparable quality to the original heuristic. Our approach, called Savant, is inspired from the savant syndrome. Its concurrency model is based on Map-Reduce. The approach is evaluated with the well-known Min-Min heuristic. Experimental results on two problem sizes are promising, the produced algorithm is able to find solutions of comparable quality.

This article addresses a virtual machine (VM) allocation problem that appears in a novel business model for cloud computing. In this model, a cloud service broker owns a number of cloud reserved instances that outsources to its customers as cheap as on-demand VMs. The objective of the broker is to efficiently manage its reserved resources to maximize its revenue. We enhance the previous definition of the problem by considering more realistic parameters: geographical localization of resources and users, different types of applications, and data transfer costs. We propose a set of heuristics to solve the optimization problem of maximizing the cloud provider profit while offering appropriate Quality-of-Service to the users. The experimental analysis is performed over different scenarios using real data from cloud providers.

Computer-aided design (CAD) is a technological revolution, very powerful and with large applicability to problem solving. It is essential in many different disciplines ranging from architecture to education, medicine, physics, or gaming. In this work, we propose a novel CAD tool, called CADDi, to assist in the design of electric diagrams in the educational context. We are applying the theory of formal languages to create WDLang, an easy-to-use, highly expressive, unequivocal, and correct programming language for designing electric circuits. This programming language is the cornerstone of CADDi, which automatically generates the equivalent ladder diagram (explains the circuit operation) to the programmed circuit, offering additional features that allow analysis of its functionality in an interactive way. It also offers a graphical interface to directly design ladder diagrams, or to modify the automatically generated ones. The existing electrical CAD tools are either very simple, e.g., for creating good-looking diagrams with no functionality, or too complex, for professional systems design. CADDi is extremely useful for learning purposes. It assists users on how to generate ladder diagrams, and on understanding the behavior of electrical circuits. Additionally, it serves as an assessment tool for self-evaluation in the translation from wiring diagrams to ladder ones. In order to make CADDi highly accessible, it was implemented as a web page.

This article introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary architecture, the population is split into several subpopulations or islands, each of them being in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. Evaluation of complete solutions is achieved through cooperation, i.e., all subpopulations share a subset of their current partial solutions. Our purpose is to study how the performance of the cooperative coevolutionary multi-objective approaches can be drastically increased with respect to their corresponding original versions. This is specially interesting for solving complex problems involving a large number of variables, since the problem decomposition performed by the model at the island level allows for much faster executions (the number of variables to handle in every island is divided by the number of islands). We conduct a study on a real-world problem related to grid computing, the bi-objective robust scheduling problem of independent tasks. The goal in this problem is to minimize makespan (i.e., the time when the latest machine finishes its assigned tasks) and to maximize the robustness of the schedule (i.e., its tolerance to unexpected changes on the estimated time to complete the tasks). We propose a parallel, multithreaded implementation of the coevolutionary algorithms and we have analyzed the results obtained in terms of both the quality of the Pareto front approximations yielded by the techniques as well as the resulting speedups when running them on a multicore machine.

In this paper, we present two new parallel algorithms to solve large instances of the scheduling of independent tasks problem. First, we describe a parallel version of the Min-min heuristic. Second, we present GraphCell, an advanced parallel cellular genetic algorithm (CGA) for the GPU. Two new generic recombination operators that take advantage of the massive parallelism of the GPU are proposed for GraphCell. A speedup study shows the high performance of the parallel Min-min algorithm in the GPU versus several CPU versions of the algorithm (both sequential and parallel using multiple threads). GraphCell improves state-of-the-art solutions, especially for larger problems, and it provides an alternative to our GPU Min-min heuristic when more accurate solutions are needed, at the expense of an increased runtime.

This paper presents a new parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. The proposed approach, called CCSMPSO, is based on a new design and implementation of SMPSO in a cooperative coevolutionary (CC) framework. In such an architecture, the population is split into several subpopulations, which are in turn in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. We compare our work with two different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II and CCSPEA2, as well as the original SMPSO in order to demonstrate its effectiveness. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better and comparable results in terms of solution quality, when compared to the other two CC algorithms and SMPSO, respectively. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front.

The energy consumption of large data centers has been increasing for the last decades and currently is a major concern for economic and environmental reasons. Accurate scheduling of the data center operation and use of renewable energy sources present themselves as promising solutions for this problem. In this paper we study the problem of scheduling workflows of tasks in distributed heterogeneous data centers which are partially powered by renewable energy sources. This problem takes into account quality of service, infrastructure usage, and power consumption of machines and cooling devices. We propose a mathematical model for accurate scheduling solutions.

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic.

This article presents a multiobjective approach for scheduling large workflows in distributed datacenters. We consider a realistic scheduling scenario of distributed cluster systems composed of multi-core computers, and a multi-objective formulation of the scheduling problem to minimize makespan, energy consumption and deadline violations. The studied schedulers follow a two-level schema: in the higher-level, we apply a multiobjective heuristic and a multiobjective metaheuristic, to distribute jobs between clusters; in the lower-level, specific backfilling-oriented scheduling methods are used for task scheduling locally within each cluster, considering precedence constraints. A new model for energy consumption in multi-core computers is applied. The experimental evaluation performed on a benchmark set of large workloads that model different realistic high performance computing applications demonstrates that the proposed multiobjective schedulers are able to improve both the makespan and energy consumption of the schedules when compared with a standard Optimistic Load Balancing Round Robin approach.

Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to close ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore, in a better performance of the algorithm. However, it supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. We propose in this work two innovative cGAs with new adaptive techniques that allow removing the neighborhood and population shape from the algorithm's configuration. As a result, the new adaptive cGAs are highly competitive (statistically) with all the compared cGAs in terms of the average solutions found in the continuous and combinatorial domains, while finding, in general, the best solutions for the considered problems, and with less computational effort.

The sensitivity analysis of a Cellular Genetic Algorithm (CGA) with local search is used to design a new and faster heuristic for the problem of mapping independent tasks to a distributed system (such as a computer cluster or grid) in order to minimize makespan (the time when the last task finishes). The proposed heuristic improves the previously known Min-Min heuristic. Moreover, the heuristic finds mappings of similar quality to the original CGA but in a significantly reduced runtime (1,000 faster). The proposed heuristic is evaluated across twelve different classes of scheduling instances. In addition, a proof of the energy-efficiency of the algorithm is provided. This convergence study suggests how additional energy reduction can be achieved by inserting low power computing nodes to the distributed computer system. Simulation results show that this approach reduces both energy consumption and makespan.

Mobile ad hoc networks (or MANETs) are wireless networks that are spontaneously created by the neighbouring devices, without the use of any kind of infrastructure. These devices usually rely on batteries, so the lifetime of this type of network highly depends on the energy consumption of the devices composing it. Therefore, the optimisation of the energy consumption is a must in a realistic MANET. We deal in this paper with the optimisation of enhanced distance based (or EDB) broadcasting protocol for MANETs. EDB is an improved version of DB (distance based protocol), a state-of-the-art broadcasting protocol for MANETs, that is focussed on saving the energy used by the devices in the dissemination process (an essential component in MANETs), without degrading the network connectivity or the performance in the coverage of the broadcasting process. A set of parameters were identified in EDB and optimised using CellDE, a hybrid multi-objective optimisation algorithm, to maximise the coverage of the broadcast while minimising at the same time both the energy consumption and the broadcast time. The ns3 simulator was used to evaluate the different configurations of EDB. As a result, 100 different solutions are provided for every studied network, covering a wide range of design options; all of them have quality better than or similar to that provided by EDB with previously recommended values for its parameters.

Differential evolution (DE) algorithms compose an efficient type of evolutionary algorithm (EA) for the global optimization domain. Although it is well known that the population structure has a major influence on the behavior of EAs, there are few works studying its effect in DE algorithms. In this paper, we propose and analyze several DE variants using different panmictic and decentralized population schemes. As it happens for other EAs, we demonstrate that the population scheme has a marked influence on the behavior of DE algorithms too. Additionally, a new operator for generating the mutant vector is proposed and compared versus a classical one on all the proposed population models. After that, a new heterogeneous decentralized DE algorithm combining the two studied operators in the best performing studied population structure has been designed and evaluated. In total, 13 new DE algorithms are presented and evaluated in this paper. Summarizing our results, all the studied algorithms are highly competitive compared to the state-of-the-art DE algorithms taken from the literature for most considered problems, and the best ones implement a decentralized population. With respect to the population structure, the proposed decentralized versions clearly provide a better performance compared to the panmictic ones. The new mutation operator demonstrates a faster convergence on most of the studied problems versus a classical operator taken from the DE literature. Finally, the new heterogeneous decentralized DE is shown to improve the previously obtained results, and outperform the compared state-of-the-art DEs.

We propose the use of a new algorithm to solve multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single-objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators from evolutionary algorithms. AbYSS incorporates typical concepts from the multiobjective field, such as Pareto dominance, density estimation, and an external archive to store the nondominated solutions. We evaluate AbYSS with a standard benchmark including both unconstrained and constrained problems, and it is compared with two state-of-the-art multiobjective optimizers, NSGA-II and SPEA2. The results obtained indicate that, according to the benchmark and parameter settings used, AbYSS outperforms the other two algorithms as regards the diversity of the solutions, and it obtains very competitive results according to the convergence to the true Pareto fronts and the hypervolume metric.

Computational grids are an important emerging paradigm for large-scale distributed computing. As grid systems become more wide-spread, techniques for efficiently exploiting the large amount of grid computing resources become increasingly indispensable. A key aspect in order to benefit from these resources is the scheduling of jobs to grid resources. Due to the complex nature of grid systems, the design of efficient grid schedulers becomes challenging since such schedulers have to be able to optimize many conflicting criteria in very short periods of time. This problem has been tackled in the literature by several different metaheuristics, and our main focus in this work is to develop a new highly competitive technique with respect to the existing ones. For that, we exploit the capabilities of cellular memetic algorithms (cMAs), a kind of memetic algorithm with structured population, for obtaining efficient batch schedulers for grid systems, and the obtained results will be compared versus the state of the art. A careful design of the cMA methods and operators for the problem yielded to an efficient and robust implementation. Our experimental study, based on a known static benchmark for the problem, shows that this heuristic approach is able to deliver very high quality planning of jobs to grid nodes and thus it can be used to design efficient dynamic schedulers for real grid systems. Such dynamic schedulers can be obtained by running the cMA-based scheduler in batch mode for a very short time to schedule jobs arriving in the system since the last activation of the cMA scheduler.

Mobile Ad Hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, broadcasting becomes an operation of capital importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multi-objective problem targeting three goals: reaching as many devices as possible, minimizing the network utilization, and reducing the duration time of the broadcasting process. In this paper, we study the fine-tuning of broadcasting strategies by using a cellular multi-objective genetic algorithm (cMOGA) which computes a Pareto front of solutions to empower a human designer with the ability of choosing the preferred configuration for the network. We define two formulations of the problem, one with three objectives and another one with two objectives plus a constraint. For our tests, a benchmark of three realistic environments for metropolitan MANETs has been defined. Our experiments using a complex and realistic MANET simulator reveal that cMOGA is a promising approach to solve the optimum broadcasting problem.