A cycle-based evolutionary algorithm for the fixed-charge capacitated multi-commodity network design problem

This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through selecting arcs, with an objective of minimizing the fixed costs of the selected arcs plus the variable costs of the flows on each arc. The proposed algorithm evolves a pool of solutions using principles of scatter search, interlinked with an iterated local search as an improvement method. New cycle-based neighborhood operators are presented which enable complete or partial re-routing of multiple commodities. An efficient perturbation strategy, inspired by ejection chains, is introduced to perform local compound cycle-based moves to explore different parts of the solution space. The algorithm also allows infeasible solutions violating arc capacities while performing the "ejection cycles", and subsequently restores feasibility by systematically applying correction moves. Computational experiments on benchmark MCNDP instances show that the proposed solution method consistently produces high-quality solutions in reasonable computational times

Negatively Correlated Search

Evolutionary Algorithms (EAs) have been shown to be powerful tools for complex optimization problems, which are ubiquitous in both communication and big data analytics. This paper presents a new EA, namely Negatively Correlated Search (NCS), which maintains multiple individual search processes in parallel and models the search behaviors of individual search processes as probability distributions. NCS explicitly promotes negatively correlated search behaviors by encouraging differences among the probability distributions (search behaviors). By this means, individual search processes share information and cooperate with each other to search diverse regions of a search space, which makes NCS a promising method for non-convex optimization. The cooperation scheme of NCS could also be regarded as a novel diversity preservation scheme that, different from other existing schemes, directly promotes diversity at the level of search behaviors rather than merely trying to maintain diversity among candidate solutions. Empirical studies showed that NCS is competitive to well-established search methods in the sense that NCS achieved the best overall performance on 20 multimodal (non-convex) continuous optimization problems. The advantages of NCS over state-of-the-art approaches are also demonstrated with a case study on the synthesis of unequally spaced linear antenna arrays

A phenotypic analysis of three population-based metaheuristics

Metaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Improved scatter search algorithm based on meerkat clan algorithm to solve NP-hard problems

A modified Scatter Search (SS) algorithm based on Meerkat Clan Algorithm (MCA) has been presented in this paper. SS is one of the important metaheuristic algorithms, while the MCA is one of the recent swarm intelligence algorithms. The modified SS algorithm, including the main steps of MCA, through it the diversity and exploration of SS-MCA's solutions, have improved. The proposed algorithm has been applied to two important NP-Hard problems (Travelling Salesman Problem (TSP) and Flexible Job Shop Scheduling Problem (FJSSP)) to verify the performance of SS-MCA. The experimental results show that the performance of SS-MCA is better than both SS and MCA, respectively

Adaptive Scatter Search to Solve the Minimum Connected Dominating Set Problem for Efficient Management of Wireless Networks

An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. We tested the performance of our proposed algorithm using different benchmark-test-graph sets available in the literature. Experiments results show that our proposed algorithm gave good results in terms of solution quality

Meta-heuristic resource constrained project scheduling: solution space restrictions and neighbourhood extensions

The resource-constrained project scheduling problem (RCPSP) has been extensively investigated during the past decades. Due to its strongly NP-hard status and the need for solving large realistic project instances, the recent focus has shifted from exact optimisation procedures to (meta-) heuristic approaches. In this paper, we extend some existing state-of-the-art RCPSP procedures in two ways. First, we extensively test a decomposition approach that splits problem instances into smaller sub-problems to be solved with an (exact or heuristic) procedure, and re-incorporates the obtained solutions for the sub-problems into the solution of the main problem, possibly leading to an overall better solution. Second, we study the influence of an extended neighbourhood search on the performance of a meta-heuristic procedure. Computational results reveal that both techniques are valuable extensions and lead to improved results

Better Physical Activity Classification using Smartphone Acceleration Sensor

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities

New computational results for the nurse scheduling problem: A scatter search algorithm

In this paper, we present a scatter search algorithm for the well-known nurse scheduling problem (NSP). This problem aims at the construction of roster schedules for nurses taking both hard and soft constraints into account. The objective is to minimize the total preference cost of the nurses and the total penalty cost from violations of the soft constraints. The problem is known to be NP-hard. The contribution of this paper is threefold. First, we are, to the best of our knowledge, the first to present a scatter search algorithm for the NSP. Second, we investigate two different types of solution combination methods in the scatter search framework, based on four different cost elements. Last, we present detailed computational experiments on a benchmark dataset presented recently, and solve these problem instances under different assumptions. We show that our procedure performs consistently well under many different circumstances, and hence, can be considered as robust against case-specific constraints. Keywords: meta-heuristics, scatter search, nurse schedulin