The paper describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its' simplicity as it does require the setting of only very few parameters. The implementation of the Pareto Iterated Local Search metaheuristic is based on the MOOPPS computer system of local search heuristics for multi-objective scheduling which has been awarded the European Academic Software Award 2002 in Ronneby, Sweden (http://www.easa-award.net/, http://www.bth.se/llab/easa_2002.nsf)
Helmut Schmidt University — University of the Federal Armed Forces Hamburg（赫尔穆特·施密特大学——汉堡联邦国防军大学）
The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives and compared to other local search approaches. While the obtained results are encouraging in terms of their quality, another positive attribute of the …
Refined Iterated Pareto Greedy for Energy-aware Hybrid Flowshop Scheduling with Blocking Constraints
Department of Computer Science（计算机科学系）；University College Cork（科尔克大学）；Process, Energy and Transport Engineering（过程、能源与运输工程）；Munster Technological University（穆斯林技术大学）
Massachusetts Institute of Technology（麻省理工学院）；University of Southampton（南安普顿大学）；Universidade Federal da Paraíba（帕拉伊巴联邦大学）
The Clustered Vehicle Routing Problem (CluVRP) is a variant of the Capacitated Vehicle Routing Problem in which customers are grouped into clusters. Each cluster has to be visited once, and a vehicle entering a cluster cannot leave it until all customers have been visited. This article presents two alternative hybrid metaheuristic algorithms for the CluVRP. The first algorithm is based on an Iterated Local Search algorithm, in which only feasible solutions are explored and problem-specific local search moves are utilized. The second algorithm is a Hybrid Genetic Search, for which the shortest …
University of Lausanne（洛桑大学）；Université Nice-Sophia Antipolis（尼斯-索菲亚·安提波利斯大学）；INRIA Lille Nord Europe（法国国家信息与自动化研究所里尔-北欧分所）；University of Nottingham（诺丁汉大学）
Local Optima Networks (LONs) have been recently proposed as an alternative model of combinatorial fitness landscapes. The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. A new set of metrics can be derived from this model that capture the distribution and connectivity of the local optima in the underlying configuration space. This paper departs from the descriptive analysis of local optima networks, and actively studies the corre…
Universidade Federal da Bahia（巴伊亚联邦大学）；University of Antwerp（安特卫普大学）
The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network such that hydraulic constraints are satisfied and the total cost is minimized. Traditionally, such design decisions are made on the basis of expert experience. When networks increase in size, however, rules of thumb will rarely lead to near optimal decisions. Over the past thirty years, a large number of techniques have been developed to tackle the problem of optimally designing a water distribution network. In this paper, we tackle the NP-hard water distr…
Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Birjand University of Technology（比尔詹德工业大学）
Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a better Pareto optimal solution, metaheuristic optimization methods are widely used in many studies. However, the main drawback is the exploration of a large search space. Another problem with multi-objective feature selection approaches is the interaction between features. Selecting …
IBM Research（IBM研究院）；Instituto Superior Técnico（高等技术学院）；IBM Software Group（IBM软件集团）
Machine Reassignment is a challenging problem for constraint programming (CP) and mixed-integer linear programming (MILP) approaches, especially given the size of data centres. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP or MILP to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study under which conditions a mixed-integer optimisation solver, such as IBM ILOG CPLEX, can be used for the…
The bi-objective winner determination problem (2WDP-SC) of a combinatorial procurement auction for transport contracts is characterized by a set B of bundle bids, with each bundle bid b in B consisting of a bidding carrier c_b, a bid price p_b, and a set tau_b transport contracts which is a subset of the set T of tendered transport contracts. Additionally, the transport quality q_{t,c_b} is given which is expected to be realized when a transport contract t is executed by a carrier c_b. The task of the auctioneer is to find a set X of winning bids (X subset B), such that each transport contract…
National University of Defense Technology（国防科技大学）；Leiden University（莱顿大学）；De Montfort University（德蒙福特大学）；Instituto Universitário de Lisboa (ISCTE-IUL)（里斯本大学学院）；Polytechnic Institute of Leiria（莱里亚理工学院）；University of Münster（明斯特大学）
User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming. Recently, it is integrated in multi-objective metaheuristics (MOMH), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications on preference-based multi-objective metaheuristics has increased rapidly over the past decades. There already exist various preference handling met…
Instituto de Computação – Universidade Federal Fluminense（联邦弗卢米嫩塞大学计算机学院）；Departamento de Engenharia Industrial – Pontifícia Universidade Católica do Rio de Janeiro（天主教里约热内卢大学工业工程系）；Departamento de Informática – Pontifícia Universidade Católica do Rio de Janeiro（天主教里约热内卢大学计算机系）；Departamento de Computação – Universidade Federal de Ouro Preto（联邦乌贝兰迪亚大学计算机系）；Department of Business Administration – Universität Wien（维也纳大学工商管理系）
We consider the multi-vehicle one-to-one pickup and delivery problem with split loads, a NP-hard problem linked with a variety of applications for bulk product transportation, bike-sharing systems and inventory re-balancing. This problem is notoriously difficult due to the interaction of two challenging vehicle routing attributes, "pickups and deliveries" and "split deliveries". This possibly leads to optimal solutions of a size that grows exponentially with the instance size, containing multiple visits per customer pair, even in the same route. To solve this problem, we propose an iterated lo…
University of Toronto Scarborough（多伦多大学士嘉堡分校）；Rotman School of Management, University of Toronto（多伦多大学罗特曼管理学院）；Universidad Adolfo Ibáñez（阿道夫·伊巴涅斯大学）
We consider a recently introduced class of network construction problems where edges of a transportation network need to be constructed by a server (construction crew). The server has a constant construction speed which is much lower than its travel speed, so relocation times are negligible with respect to construction times. It is required to find a construction schedule that minimizes a non-decreasing function of the times when various connections of interest become operational. Most problems of this class are strongly NP-hard on general networks, but are often tree-efficient, that is, polyn…
Consejo Nacional de Ciencia y Tecnología - Conacyt（国家科学与技术委员会 - Conacyt）；INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación（INFOTEC 信息与通信技术创新研究中心）；CentroGEO Centro de Investigación en Ciencias de Información Geoespacial（CentroGEO 地理空间信息科学研究中心）
This manuscript introduces an autotuned algorithm for searching nearest neighbors based on neighbor graphs and optimization metaheuristics to produce Pareto-optimal searches for quality and search speed automatically; the same strategy is also used to produce indexes that achieve a minimum quality. Our approach is described and benchmarked with other state-of-the-art similarity search methods, showing convenience and competitiveness.
University of Nice Sophia Antipolis（尼斯索菲亚·安提波利斯大学）；CNRS（法国国家科学研究中心）；INRIA Lille-Nord Europe（法国国家信息与自动化研究所里尔-北欧洲分部）；Université Lille 1（里尔第一大学）
In this paper, we conduct a fitness landscape analysis for multiobjective combinatorial optimization, based on the local optima of multiobjective NK-landscapes with objective correlation. In single-objective optimization, it has become clear that local optima have a strong impact on the performance of metaheuristics. Here, we propose an extension to the multiobjective case, based on the Pareto dominance. We study the co-influence of the problem dimension, the degree of non-linearity, the number of objectives and the correlation degree between objective functions on the number of Pareto local o…
Universidade da Beira Interior（贝拉内政大学）；NOVA LINCS（NOVA LINCS实验室）
Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. While different images with different quality consume different amounts of energy, there are no straightforward methods to calculate the energy consumption of an operation in a typical image. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. Then, we propose a multi-objective strategy to e…
A Random-Key Optimizer for Combinatorial Optimization
Federal U. of São Paulo（巴西圣保罗联邦大学）；Amazon Advanced Solutions Lab（亚马逊高级解决方案实验室）；DIMACS；University of Southampton（南安普顿大学）；Federal U. of Pernambuco（巴西佩雷布鲁克联邦大学）
Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no feature learning method has been proposed for such category of techniques. Here, we present an unsupervised feature learning mechanism that works on datasets with binary features. First, the dataset is map…
