Multi-objective Metaheuristics Research Articles

Using multi-objective metaheuristics for the optimal selection of positioning systems

The interworking between cellular and wireless local area networks, as well as the spreading of mobile devices equipped with several positioning technologies pave the ground to new and more favorable indoor/outdoor location-based services (LBSs). Thus, wireless internet service providers are required to take several positioning methods into account at the same time, to leverage the different features of existing technologies. This would allow providing LBSs satisfying the user-required quality of position in terms of accuracy, privacy, power consumption, and often, conflicting features. Therefore, this paper presents GlobalPreLoc, a multi-objective strategy for the dynamic and optimal selection of positioning technologies. The strategy exploits a pattern-mining algorithm for future position prediction combined with conventional multi-objective evolutionary algorithms, for choosing continuously the best location providers, accounting for the user requirements, the terminal capabilities, and the surrounding positioning infrastructures. To practically implement the strategy, we also designed an architecture based on secure user plane location specification to provide indoor and outdoor LBSs in interworking wireless networks exploiting GlobalPreLoc features.

Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks

This paper deals with how to efficiently deploy energy-harvesting Relay Nodes in previously established low-cost static Wireless Sensor Networks (WSNs), assuming a single-tiered network model. The purpose is to optimise three conflicting objectives: Average Energy Cost, Average Sensitivity Area, and Network Reliability. This is the so-called Relay Node Placement Problem (RNPP), which is an NP-hard optimisation problem. We find many works assuming heuristics in the current literature. However, it is not the case for metaheuristics, which usually provide good results solving such complex problems. This situation led us to consider a wide range of MultiObjective (MO) metaheuristics: the two standard Genetic Algorithms NSGA-II and SPEA2, the trajectory algorithm MO-VNS, the algorithm based on decomposition MOEA/D, and two novel swarm intelligence algorithms MO-ABC and MO-FA, which are based on the behaviour of honey bees and fireflies, respectively. These metaheuristics are applied to optimise a freely available data set. The results obtained are analysed considering two MO metrics: hypervolume and set coverage. Through a widely accepted statistical methodology, we conclude that MO-FA provides the best performance on average. We also study the efficiency of this approach, verifying that it is a good strategy to optimise such networks, including some limitations. Finally, we compare this proposal to another author approach, which assumes a heuristic.

Modified Multiobjective Metaheuristics for Sparse Traffic Grooming in Optical WDM Mesh Networks

Abstract Now days the number of users that use data networks has increased tremendously and this demand will keep on increasing. This has made an increase of bandwidth necessary. Our current data networks are unable to support this growth, so better solution is to use optical data networks, in which the bandwidth is over 50Tbps. Data network provides also provides a huge bandwidth but the maximum speed for an end user is only a few Gbps which is speed of their devices. So if we use wavelength division multiplexing, maximum bandwidth utilization will be done because of simultaneous transmission of data. Traffic grooming problem is a NP-hard problem, so we need to use metaheuristics. DEPT and MOVNS have been proved to provide most optimal solutions. But these were implemented earlier on full grooming network. This paper proposes variants of DEPT and MOVNS using sparse grooming network which will provide better results in terms of throughput and propagation delay.

Multi-Objective Predictive Control : A Solution Using Metaheuristics

The application of multi objective model predictive control approaches is significantly limited with computation time associated with optimization algorithms. Metaheuristics are general purpose heuristics that have been successfully used in solving difficult optimization problems in a reasonable computation time. In this work , we use and compare two multi objective metaheuristics, Multi-Objective Particle swarm Optimization, MOPSO, and Multi-Objective Gravitational Search Algorithm, MOGSA, to generate a set of approximately Pareto-optimal solutions in a single run. Two examples are studied, a nonlinear system consisting of two mobile robots tracking trajectories and avoiding obstacles and a linear multi variable system. The computation times and the quality of the solution in terms of the smoothness of the control signals and precision of tracking show that MOPSO can be an alternative for real time applications.

Refined ranking relations for selection of solutions in multi objective metaheuristics

Abstract Two methods for ranking of solutions of multi objective optimization problems are proposed in this paper. The methods can be used, e.g. by metaheuristics to select good solutions from a set of non dominated solutions. They are suitable for population based metaheuristics to limit the size of the population. It is shown theoretically that the ranking methods possess some interesting properties for such applications. In particular, it is shown that both methods form a total preorder and are both refinements of the Pareto dominance relation. An experimental investigation for a multi objective flow shop problem shows that the use of the new ranking methods in a Population-based Ant Colony Optimization algorithm and in a genetic algorithm leads to good results when compared to other methods.

Distributed Multi-Objective Metaheuristics for Real-World Structural Optimization Problems

The design of bar structures in civil engineering is a complex problem when dealing with real-world structures. An approach to deal with these problems is to apply metaheuristics, which are stochastic methods based on iteratively producing and evaluating tentative solutions. In particular, we focus on multi-objective metaheuristics, as we consider two goals to be minimized: the weight and the deflection of the structure. When applying these techniques to real-world problems, running a metaheuristic for several thousands of evaluations may require many days on a single processor. In this paper, we develop distributed master/slave versions of four multi-objective metaheuristics that are representative of the state-of-the-art and apply them to optimize the design of two instances of a cable-strayed bridge. Our study reveals that our parallel proposals are able to effectively use up to 450 cores, providing accurate solutions in a short time.

Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints

In this paper, a three-stage assembly flow shop scheduling problem with machine availability constraints is taken into account. Two objectives of minimising total weighted completion times (flow time) and minimising sum of weighted tardiness and earliness are simultaneously considered. To describe this problem, a mathematical model is presented. The problem is generalisation of three-machine flow shop scheduling problem and two-stage assembly flow shop scheduling problem. Since these problems are known to be NP-hard, the considered problem is also strongly NP-hard. Therefore, two multi-objective meta-heuristics are presented to efficiently solve this problem in a reasonable amount of time. Comprehensive computational experiments are performed to illustrate the performance of the presented algorithms.

Integrating a multi-objective optimization framework into a structural design software

In this paper, we present a tool combining two software applications aimed at optimizing structural design problems of the civil engineering domain. Our approach lies in integrating an application for designing 2D and 3D bar structures, called Ebes, with the jMetal multi-objective optimization framework. The result is a software package that helps civil engineers to create bar structures which can be optimized further with multi-objective metaheuristics according to different goals, such as minimizing the structure weight and minimizing the deformation. The main features of both Ebes and jMetal are described and how they are combined together in one single tool is explained. Finally a case study to illustrate how the application works is presented.

Generation of the exact Pareto set in Multi-Objective Traveling Salesman and Set Covering Problems

The calculation of the exact set in Multi-Objective Combinatorial Optimization (MOCO) problems is one of the most computationally demanding tasks as most of the problems are NP-hard. In the present work we use AUGMECON2 a Multi-Objective Mathematical Programming (MOMP) method which is capable of generating the exact Pareto set in Multi-Objective Integer Programming (MOIP) problems for producing all the Pareto optimal solutions in two popular MOCO problems: The Multi-Objective Traveling Salesman Problem (MOTSP) and the Multi-Objective Set Covering Problem (MOSCP). The computational experiment is confined to two-objective problems that are found in the literature. The performance of the algorithm is slightly better to what is already found from previous works and it goes one step further generating the exact Pareto set to till now unsolved problems. The results are provided in a dedicated site and can be useful for benchmarking with other MOMP methods or even Multi-Objective Meta-Heuristics (MOMH) that can check the performance of their approximate solution against the exact solution in MOTSP and MOSCP problems.

Optimizing of bullwhip effect and net stock amplification in three-echelon supply chains using evolutionary multi-objective metaheuristics

In this article, we intend to model and optimize the bullwhip effect (BWE) and net stock amplification (NSA) in a three-stage supply chain consisting of a retailer, a wholesaler, and a manufacturer under both centralized and decentralized scenarios. In this regard, firstly, the causes of BWE and NSA are mathematically formulated using response surface methodology (RSM) as a multi-objective optimization model that aims to minimize the BWE and NSA on both chains. The simultaneous analysis of the BWE and NSA is considered as the main novelty of this paper. To tackle the addressed problem, we propose a novel multi-objective hybrid evolutionary approach called MOHES; MOHES is a hybrid of two known multi-objective algorithms i.e. multi-objective electro magnetism mechanism algorithm (MOEMA) and population-based multi-objective simulated annealing (PBMOSA). We applied a co-evolutionary strategy for this purpose with eligibility of both algorithms. Proposed MOHES is compared with three common and popular algorithms (i.e. NRGA, NSGAII, and MOPSO). Since the utilized algorithms are very sensitive to parameter values, RSM with the multi-objective decision making (MODM) approach is employed to tune the parameters. Finally, the hybrid algorithm and the singular approaches are compared together in terms of some performance measures. The results indicate that the hybrid approach achieves better solutions when compared with the others, and also the results show that in a decentralized chain, the order batching factor and the demand signal processing in wholesaler are the most important factors on BWE. Conversely, in a centralized chain, factors such as rationing, shortage gaming, and lead time are the most effective at reducing the BWE.

Two Multiobjective Metaheuristics for Solving the Integrated Problem of Frequencie Calculation and Departures Planning in an Urban Transport System

(ProQuest: ... denotes formulae omitted.)1. Introduction.In this paper is addressed the public transport planning problem. This is a process which is usually divided into four phases: network design, timetabling construction, vehicle scheduling, and crew scheduling. Usually, these phases are executed sequentially. Here in the paper, we are tackling the bus timetable construction problem of an urban bus transport network. This is usually accomplished in three steps: first for each scenario (covering a concrete planning period) bus frequencies are calculated for each route in the network, then bus departures are settled for each route in the network based on previously calculated frequencies. This is then adjusted for getting acceptable timetables for planners.The main scientific contribution of this work is the development of an integrated multi-objective mixed integer lineal mathematical model to construct multi-period urban bus timetables, which also allows smooth transitions between adjacent planning periods with different demand.Recently Ibarra-Rojas & Rios-Solis (2012) have shown formally that the timetabling problem is NP-Hard, so we implemented two multi-objective metaheuristics to explore the effectiveness of the proposed model. We designed an experiment for testing the heuristics with generated random instances.The timetabling problem has been tackled in the literature from different approaches. Ceder (2007) proposes an exact methods for creating a timetable with maximal synchronization. Also, Eranki (2004), proposes a model to create timetables with maximal synchronization using time windows. She used a heuristic method to solve the problem, but she did not consider multiple criteria. Also, other authors consider maximization of synchronization as a key objective in urban transport planning. Among them, Paunovic (2013) showed a positive correlation between children blood pressure and road traffic noise, transit density and public transport. Burke (2011) also advocates the importance of taking into consideration passenger transfer as a measure of quality for an urban transport system, which indirectly calls for synchronization maximization. Another important measure of quality for urban transport planning is quality of service from the users' perspective (see Ibeas & Cecin, 2011). Ibeas & Cecin, (2011) concluded that the most important variables when defining quality of public transport from the users' perspective are waiting time, journey time and above all, level of occupancy. Recently, this claim has been a subject of research studies by some researchers. For example, Barra et al (2007) presented a model considering different characteristics of the transport system (passenger requirements, budget constraints, level of service). There are other research works on timetabling problem in which the authors use metaheuristics like GRASP. Among these is Mauttone & Urquhart (2009) who developed a metaheuristic based on GRASP for optimizing simultaneously different objectives for passengers and schedulers.In literature, there are approaches such as Szeto & Wu (2011) that combine two phases of the urban transport process. Szeto & Wu (2011) propose a simultaneously integrated solution for the bus network design and frequency setting problems using a genetic algorithm (GA) that tackles the route network design problem. GA is hybridized with a neighborhood search heuristic which tackles the frequency setting problem. Also in Cipriani et al. (2012), network design and frequency calculation are integrated for optimizing passenger transfer, among other impact measures. There are also approaches for solving two phases sequentially. A good example is Chakroborty (2003) who combines the transit routing and scheduling phases using a genetic algorithm. In his approach, he tries to minimize the transfer time and the waiting time. Another research that combines several phases is the one proposed by Zhao & Zeng (2008). …

An Efficient Multi-objective Meta-heuristic Method for Probabilistic Transmission Network Planning

In this paper, a new method is proposed for probabilistic transmission network expansion planning in Smart Grid. The proposed method makes use of Controlled Nondominated Sorting Genetic Algorithm (CNSGA-II) of multi-objective meta-heuristics (MOMH) to calculate a set of the Pareto solutions. In recent years, electric power networks increase the degree of uncertainties due to new environment of Smart Grid with renewable energy, distributed generation, Demand Response (DR), etc. Smart grid planners are interested in improving power supply reliability of transmission networks so that probabilistic expansion planning approaches are required. This paper focuses on a multi-objective problem in probabilistic transmission network expansion planning. The multi-objective optimization problem may be expressed as multi-metaheuristic formulation that evaluates a set of the Pareto solutions in Monte Carlo Simulation (MCS). In this paper, CNSGA-II is used to calculate a set of the Pareto Solutions. The proposed method is successfully applied to the IEEE 24-bus reliability test system.

Robust Scheduling for Berth Allocation and Quay Crane Assignment Problem

Decision makers must face the dynamism and uncertainty of real-world environments when they need to solve the scheduling problems. Different incidences or breakdowns, for example, initial data could change or some resources could become unavailable, may eventually cause the infeasibility of the obtained schedule. To overcome this issue, a robust model and a proactive approach are presented for scheduling problems without any previous knowledge about incidences. This paper is based on proportionally distributing operational buffers among the tasks. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems. The dynamism and uncertainty are managed by assessing the robustness of the schedules. The robustness is introduced by means of operational buffer times to absorb those unknown incidences or breakdowns. Therefore, this problem becomes a multiobjective combinatorial optimization problem that aims to minimize the total service time, to maximize the buffer times, and to minimize the standard deviation of the buffer times. To this end, a mathematical model and a new hybrid multiobjective metaheuristic is presented and compared with two well-known multiobjective genetic algorithms: NSGAII and SPEA2+.

Modeling and solving the bi-objective minimum diameter-cost spanning tree problem

The bi-objective minimum diameter-cost spanning tree problem (bi-MDCST) seeks spanning trees with minimum total cost and minimum diameter. The bi-objective version generalizes the well-known bounded diameter minimum spanning tree problem. The bi-MDCST is a NP-hard problem and models several practical applications in transportation and network design. We propose a bi-objective multiflow formulation for the problem and effective multi-objective metaheuristics: a multi-objective evolutionary algorithm and a fast nondominated sorting genetic algorithm. Some guidelines on how to optimize the problem whenever a priority order can be established between the two objectives are provided. In addition, we present bi-MDCST polynomial cases and theoretical bounds on the search space. Results are reported for four representative test sets.

The software project scheduling problem: A scalability analysis of multi-objective metaheuristics

Computer aided techniques for scheduling software projects are a crucial step in the software development process within the highly competitive software industry. The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime, thus involving mainly both people-intensive activities and human resources. Two major conflicting goals arise when scheduling a software project: reducing both its cost and duration. A multi-objective approach is therefore the natural way of facing the SPS problem. As companies are getting involved in larger and larger software projects, there is an actual need of algorithms that are able to deal with the tremendous search spaces imposed. In this paper, we analyze the scalability of eight multi-objective algorithms when they are applied to the SPS problem using instances of increasing size. The algorithms are classical algorithms from the literature (NSGA-II, PAES, and SPEA2) and recent proposals (DEPT, MOCell, MOABC, MO-FA, and GDE3). From the experimentation conducted, the results suggest that PAES is the algorithm with the best scalability features.

A survey of multi-objective metaheuristics applied to structural optimization

In civil and industrial engineering, structural design optimization problems are usually characterized by the presence of multiple conflicting objectives, as to get the minimum investment cost and the maximum safety of the final design. This issue makes these problems to have not only one single solution, but a set them. Such solutions represent the possible trade-offs among the different objectives to be optimized. This paper reviews the latest developments in the field of multi-objective metaheuristics for solving design problems focusing on the optimization of the topology, shape, and sizing of civil engineering structures. We review both the algorithms and the applications, and the most relevant features of the solvers and the design optimization problems are analyzed. The paper ends by addressing a number of relevant and open issues that can be the subject of further research.

Multiobjective Metaheuristics for Traffic Grooming in Optical Networks

Currently, wavelength division multiplexing technology is widely used for exploiting the huge bandwidth of optical networks. It allows simultaneous transmission of traffic on many nonoverlapping channels (wavelengths). These channels support traffic demands in the gigabits per second (Gb/s) range; however, since the majority of devices or applications only require a bandwidth of megabits per second (Mb/s), this is a waste of bandwidth. This problem is efficiently solved by multiplexing a number of low-speed traffic demands (Mb/s) onto a high-speed wavelength channel (Gb/s). This is known as the traffic grooming problem. Since traffic grooming is an NP-hard problem, in this paper, we propose two novel multiobjective evolutionary algorithms for solving it. The selected algorithms are multiobjective variants of the standard differential evolution (DEPT) and variable neighborhood search. With the aim of ensuring the performance of our proposals, we have made comparisons with the well-known fast Nondominated Sort Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm 2, and other approaches published in the literature. After performing diverse comparisons, we can conclude that our novel approaches obtain promising results, highlighting in particular the performance of the DEPT algorithm.

Applying a multiobjective metaheuristic inspired by honey bees to phylogenetic inference

The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view about the reconstruction of ancestral evolutionary relationships among species. As a result, biologists often report different phylogenetic trees from a same dataset when considering distinct optimality principles. In this work, we detail a multiobjective swarm intelligence approach based on the novel Artificial Bee Colony algorithm for inferring phylogenies. The aim of this paper is to propose a complementary view of phylogenetics according to the maximum parsimony and maximum likelihood criteria, in order to generate a set of phylogenetic trees that represent a compromise between these principles. Experimental results on a variety of nucleotide data sets and statistical studies highlight the relevance of the proposal with regard to other multiobjective algorithms and state-of-the-art biological methods.

Intelligent grid enabled services for neuroimaging analysis

This paper reports our work in the context of the neuGRID project in the development of intelligent services for a robust and efficient Neuroimaging analysis environment. neuGRID is an EC-funded project driven by the needs of the Alzheimer's disease research community that aims to facilitate the collection and archiving of large amounts of imaging data coupled with a set of services and algorithms. By taking Alzheimer's disease as an exemplar, the neuGRID project has developed a set of intelligent services and a Grid infrastructure to enable the European neuroscience community to carry out research required for the study of degenerative brain diseases. We have investigated the use of machine learning approaches, especially evolutionary multi-objective meta-heuristics for optimising scientific analysis on distributed infrastructures. The salient features of the services and the functionality of a planning and execution architecture based on an evolutionary multi-objective meta-heuristics to achieve analysis efficiency are presented. We also describe implementation details of the services that will form an intelligent analysis environment and present results on the optimisation that has been achieved as a result of this investigation.

An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems

Generation (or a posteriori) methods in Multi-Objective Mathematical Programming (MOMP) is the most computationally demanding category among the MOMP approaches. Due to the dramatic increase in computational speed and the improvement of Mathematical Programming algorithms the generation methods become all the more attractive among today’s decision makers. In the current paper we present the generation method AUGMECON2 which is an improvement of our development, AUGMECON. Although AUGMECON2 is a general purpose method, we will demonstrate that AUGMECON2 is especially suitable for Multi-Objective Integer Programming (MOIP) problems. Specifically, AUGMECON2 is capable of producing the exact Pareto set in MOIP problems by appropriately tuning its running parameters. In this context, we compare the previous and the new version in a series of new and old benchmarks found in the literature. We also compare AUGMECON2’s performance in the generation of the exact Pareto sets with established methods and algorithms based on specific MOIP problems (knapsack, set packing) and on published results. Except from other Mathematical Programming methods, AUGMECON2 is found to be competitive also with Multi-Objective Meta-Heuristics (MOMH) in producing adequate approximations of the Pareto set in Multi-Objective Combinatorial Optimization (MOCO) problems.