Multi-objective Beam-ACO for Maximising Reliability and Minimising Communication Overhead in the Component Deployment Problem

Automated deployment of software components into hardware resources is a highly constrained optimisation problem. Hardware memory limits which components can be deployed into the particular hardware unit. Interacting software components have to be deployed either into the same hardware unit, or connected units. Safety concerns could restrict the deployment of two software components into the same unit. All these constraints hinder the search for high quality solutions that optimise quality attributes, such as reliability and communication overhead. When the optimisation problem is multi-objective, as it is the case when considering reliability and communication overhead, existing methods often fail to produce feasible results. Moreover, this problem can be modelled by bipartite graphs with complicating constraints, but known methods do not scale well under the additional restrictions. In this paper, we develop a novel multi-objective Beam search and ant colony optimisation (Beam-ACO) hybrid method, which uses problem specific bounds derived from communication, co-localisation and memory constraints, to guide the search towards feasibility. We conduct an experimental evaluation on a range of component deployment problem instances with varying levels of difficulty. We find that Beam-ACO guided by the co-localisation constraint is most effective in finding high quality feasible solutions.

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Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽

10.3390/healthcare9020126 ◽

2021 ◽

Vol 9 (2) ◽

pp. 126

Author(s):

Hai-Feng Ling ◽

Zheng-Lian Su ◽

Xun-Lin Jiang ◽

Yu-Jun Zheng

Keyword(s):

Prevention And Control ◽

Large Scale ◽

Medical Services ◽

The Novel ◽

Multi Objective ◽

Medical Supplies ◽

Problem Instances ◽

Novel Coronavirus ◽

And Control ◽

Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

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Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation

Algorithms ◽

10.3390/a14080219 ◽

2021 ◽

Vol 14 (8) ◽

pp. 219

Author(s):

Dhananjay Thiruvady ◽

Kerri Morgan ◽

Susan Bedingfield ◽

Asef Nazari

Keyword(s):

Integer Programming ◽

Gender Equity ◽

Programming Model ◽

Original Data ◽

Ant Colony ◽

Ant Colony Optimisation ◽

Integer Programming Model ◽

Problem Instances ◽

Work Integrated Learning ◽

Time Required

The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate diversity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames.

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A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

SN Computer Science ◽

10.1007/s42979-021-00512-z ◽

2021 ◽

Vol 2 (3) ◽

Author(s):

Lilla Beke ◽

Michal Weiszer ◽

Jun Chen

Keyword(s):

Shortest Path ◽

Time Budget ◽

Exact Algorithm ◽

Simple Graph ◽

Shortest Path Problem ◽

Future Application ◽

Multi Objective ◽

Trade Offs ◽

Conflicting Objectives ◽

Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

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Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽

10.3390/a12050099 ◽

2019 ◽

Vol 12 (5) ◽

pp. 99 ◽

Cited By ~ 2

Author(s):

Kleopatra Pirpinia ◽

Peter A. N. Bosman ◽

Jan-Jakob Sonke ◽

Marcel van Herk ◽

Tanja Alderliesten

Keyword(s):

Machine Learning ◽

Image Registration ◽

State Of The Art ◽

Deformable Image Registration ◽

Optimization Approach ◽

High Quality ◽

Trade Off ◽

Multi Objective ◽

Current State ◽

Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

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Multi-objective Quadratic Assignment Problem Instances Generator with a Known Optimum Solution

Parallel Problem Solving from Nature – PPSN XIII - Lecture Notes in Computer Science ◽

10.1007/978-3-319-10762-2_55 ◽

2014 ◽

pp. 559-568 ◽

Cited By ~ 1

Author(s):

Mădălina M. Drugan

Keyword(s):

Assignment Problem ◽

Quadratic Assignment Problem ◽

Quadratic Assignment ◽

Multi Objective ◽

Problem Instances ◽

Optimum Solution

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A Kriging-Assisted Light Beam Search Method for Multi-Objective Electromagnetic Inverse Problems

IEEE Transactions on Magnetics ◽

10.1109/tmag.2017.2748560 ◽

2018 ◽

Vol 54 (3) ◽

pp. 1-4 ◽

Cited By ~ 3

Author(s):

Siguang An ◽

Shiyou Yang ◽

Osama A. Mohammed

Keyword(s):

Inverse Problems ◽

Light Beam ◽

Search Method ◽

Beam Search ◽

Multi Objective

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Evolving Hard and Easy Traveling Salesman Problem Instances: A Multi-objective Approach

Lecture Notes in Computer Science - Simulated Evolution and Learning ◽

10.1007/978-3-319-13563-2_19 ◽

2014 ◽

pp. 216-227 ◽

Cited By ~ 2

Author(s):

He Jiang ◽

Wencheng Sun ◽

Zhilei Ren ◽

Xiaochen Lai ◽

Yong Piao

Keyword(s):

Traveling Salesman Problem ◽

Traveling Salesman ◽

Multi Objective ◽

Problem Instances

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Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation

Evolutionary Computation ◽

10.1162/evco.2009.17.4.17404 ◽

2009 ◽

Vol 17 (4) ◽

pp. 511-526 ◽

Cited By ~ 12

Author(s):

Thomas Tometzki ◽

Sebastian Engell

Keyword(s):

Evolutionary Algorithm ◽

Empirical Investigation ◽

Scale Up ◽

Exact Algorithm ◽

Integer Program ◽

Mixed Integer ◽

Two Stage ◽

Integer Programming Problems ◽

Problem Instances ◽

Feasible Solutions

In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.

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