Showing papers by "Christian Blum published in 2021"

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Abstract: Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers

Abstract: Peer-to-peer ridesharing enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic). Despite these very promising potential advantages, the percentage of users who currently adopt ridesharing solutions is very low, well below the adoption rate required to achieve said benefits. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who are not able to estimate the costs and the benefits of a given ridesharing adoption policy. Here we address these issues by (i) developing a novel algorithm that makes large-scale, real-time peer-to-peer ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing scenarios in terms of environmental benefits (i.e., reduction of CO2 emissions, noise pollution, and traffic congestion) and quality of service for the users. Our analysis on a real-world dataset shows that major societal benefits are expected from deploying peer-to-peer ridesharing depending on the trade-off between environmental benefits and quality of service. Results on a real-world dataset show that our approach can produce reductions up to a 70.78% in CO2 emissions and up to 80.08% in traffic congestion.

An Improved Greedy Heuristic for the Minimum Positive Influence Dominating Set Problem in Social Networks

Abstract: This paper presents a performance comparison of greedy heuristics for a recent variant of the dominating set problem known as the minimum positive influence dominating set (MPIDS) problem. This APX-hard combinatorial optimization problem has applications in social networks. Its aim is to identify a small subset of key influential individuals in order to facilitate the spread of positive influence in the whole network. In this paper, we focus on the development of a fast and effective greedy heuristic for the MPIDS problem, because greedy heuristics are an essential component of more sophisticated metaheuristics. Thus, the development of well-working greedy heuristics supports the development of efficient metaheuristics. Extensive experiments conducted on a wide range of social networks and complex networks confirm the overall superiority of our greedy algorithm over its competitors, especially when the problem size becomes large. Moreover, we compare our algorithm with the integer linear programming solver CPLEX. While the performance of CPLEX is very strong for small and medium-sized networks, it reaches its limits when being applied to the largest networks. However, even in the context of small and medium-sized networks, our greedy algorithm is only 2.53% worse than CPLEX.

Solving the Longest Common Subsequence Problem Concerning Non-Uniform Distributions of Letters in Input Strings

Abstract: The longest common subsequence (LCS) problem is a prominent NP–hard optimization problem where, given an arbitrary set of input strings, the aim is to find a longest subsequence, which is common to all input strings. This problem has a variety of applications in bioinformatics, molecular biology and file plagiarism checking, among others. All previous approaches from the literature are dedicated to solving LCS instances sampled from uniform or near-to-uniform probability distributions of letters in the input strings. In this paper, we introduce an approach that is able to effectively deal with more general cases, where the occurrence of letters in the input strings follows a non-uniform distribution such as a multinomial distribution. The proposed approach makes use of a time-restricted beam search, guided by a novel heuristic named Gmpsum. This heuristic combines two complementary scoring functions in the form of a convex combination. Furthermore, apart from the close-to-uniform benchmark sets from the related literature, we introduce three new benchmark sets that differ in terms of their statistical properties. One of these sets concerns a case study in the context of text analysis. We provide a comprehensive empirical evaluation in two distinctive settings: (1) short-time execution with fixed beam size in order to evaluate the guidance abilities of the compared search heuristics; and (2) long-time executions with fixed target duration times in order to obtain high-quality solutions. In both settings, the newly proposed approach performs comparably to state-of-the-art techniques in the context of close-to-uniform instances and outperforms state-of-the-art approaches for non-uniform instances.

Negative learning ant colony optimization for the minimum positive influence dominating set problem

Abstract: Recent research has shown that adding negative learning to ant colony optimization, in addition to the traditional positive learning mechanism, may improve the algorithms' performance significantly. In this paper we consider the application of this novel ant colony optimization variant to an NP-hard combinatorial optimization problem known as the minimum positive influence dominating set problem. This problem has applications especially in the context of social networks. Our results show, first, that the negative learning variant significantly improves over the standard ant colony optimization variant. Second, the obtained results show that our algorithm outperforms all competitors from the literature.

Negative Learning in Ant Colony Optimization: Application to the Multi Dimensional Knapsack Problem

Abstract: In this paper we continue our recent work on the development of a negative learning component for ant colony optimization, which is a metaheuristic algorithm that is mostly based on positive learning, that is, on learning from positive examples. In particular, we apply our approach to the well-known multi dimensional knapsack problem as a test case. The obtained results show that our negative learning approach significantly outperforms the standard ant colony optimization approach.

A Greedy Heuristic for Maximizing the Lifetime of Wireless Sensor Networks Based on Disjoint Weighted Dominating Sets

Abstract: Dominating sets are among the most well-studied concepts in graph theory, with many real-world applications especially in the area of wireless sensor networks. One way to increase network lifetime in wireless sensor networks consists of assigning sensors to disjoint dominating node sets, which are then sequentially used by a sleep–wake cycling mechanism. This paper presents a greedy heuristic for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Moreover, an integer linear programming model is presented. Experimental results based on a large set of 640 problem instances show, first, that the integer linear programming model is only useful for small problem instances. Moreover, they show that our algorithm outperforms recent local search algorithms from the literature with respect to both solution quality and computation time.