A bi-population based scheme for an explicit exploration/exploitation trade-off in dynamic environments

Optimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most successful and promising approaches that have addressed dynamic optimisation problems. However, managing the exploration/exploitation trade-off in EAs is still a prevalent issue, and this is due to the difficulties associated with the control and measurement of such a behaviour. The proposal of this paper is to achieve a balance between exploration and exploitation in an explicit manner. The idea is to use two equally sized populations: the first one performs exploration while the second one is responsible for exploitation. These tasks are alternated from one generation to the next one in a regular pattern, so as to obtain a balanced search engine. Besides, we reinforce the ability of our algorithm to quickly adapt after cnhanges by means of a memory of past solutions. Such a combination aims to restrain the premature convergence, to broaden the search area, and to speed up the optimisation. We show through computational experiments, and based on a series of dynamic problems and many performance measures, that our approach improves the performance of EAs and outperforms competing algorithms.Web of Science29347945

Proceedings of 2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA2020): Unifying the scientific contributions of the following conferences:SIIE’2019 & ISKO-Maghreb’2019 & CITED’2019 & TBMS’2019

International audienceThe OCTA’2019 international Multi-Conference on « Organization of Knowledge and Advanced Technologies » is a large-scale scientific event to bring together researchers and R&D professionals on ideas and common actions in the organization of knowledge while defining collaborative strategies using advanced technologies in multiple fields of research and application for society and its cultural, education, economic and industrial developments.Also, to initiate future projects in innovation in order to bring public and private institutions closer to tomorrow’s technological challenges.In OCTA’2019, the scientific projects involved in this Multi-Conference event, are:1- SIIE (https://siie2019.loria.fr/ & www.siie.fr) on « Information Systems and Economic Intelligence »,2- ISKO-Maghreb (https://isko-maghreb2019.loria.fr/ & www.isko-maghreb.org) on « Digital Sciences: impacts and challenges on Knowledge Organization »,3- CITED (https://cited2019.loria.fr/) on « Advanced Technologies, Renewable Energies and Economic Development »,4- TBMS (https://tbms2019.loria.fr/) on « Big-Data-Analytics Technologies for Strategic Management: innovation and competitiveness »,with the following state of mind:- How to strengthen alliances between multi-disciplinary and trans-disciplinary?- How to multiply skills on common study objects? - How to innovate in the solutions to found and to propose in society in respect of the sustainable development?The OCTA’2019 international Multi-Conference on « Organization of Knowledge and Advanced Technologies » aims to develop subjects like:- Information Systems: architectures, models, implementations and developments,- Economic Intelligence (or Competitive Intelligence) applying methodology, context studies and implementation of systems,- Knowledge Organization applying conceptual work, process, systems (KOS) and services,- Advanced Technologies for renewable Energies, production systems, green economy, ecological engineering, etc.- Advanced Technologies for Big Data Analytics,- Strategic Management and Systems using Big Data,- Governance Organizations applying Enterprise Strategies, Strategic Management and Economic Intelligence, etc.- “Digital Sciences”, Collective Intelligence, Digital economy on Web X.0, or Web X.Y.Z.α (ie. Web’s evolution, which aims to harness the potential of the Web in a more interactive and collaborative way, with a focus on social interaction and the assistance of an artificial intelligence),- Digital and Dematerialisation effects in the Green Economy,- Data, Big Data, Knowledge Management, Decision-making and Complexity,- Data science and new trends in Economic Development: Modern finance and technological advances, Green economy and sustainable development, Green finance, Environmental Accounting, Green marketing, Green management, e-Governance, etc.and other emergent related fields.We give interest in approaching the Humanists by subjects like:- Digital Arts & Humanities and the potential of the Creativity: Design and Model in Digital Art, Creativity process in Digital Arts, Studies in Digital Arts & Humanities and its Applications, e-Creativity, e-Art, Digital Media and Technology, Digital way to produce Art, Creativity using Digital Art Form,- Digital Arts in Business and Society: boosting the Creativity Potential for Business and Competitiveness.- Digital Humanities and impacts in research and application: “Digital Humanities” context related to aspects of the “Knowledge Organization” and management of “Science” modalities. and other related fields

A Decision Support System for Smart Health Care

The Smart City has become a renowned opportunity to improve the quality of everyday urban life activities, particularly in smart health-care domain. We address, in this paper, a very recurring problem within hospitals that consists in assigning patients to a limited number of beds. This problem becomes more complex when dealing with real-time requests, and the time factor becomes the most critical. In such situations, a set of patients arriving over time are to be examined, and their clinical states are to be well specified in order to decide whether they need admission and hospitalization or not. In case of hospitalization, the hospital staff should assign patients to beds while taking into account beds availability in terms of specialization and patient needs. All these actions should be well planned in order to maximize the quality of service in the hospitals. This challenging problem can be modeled as a dynamic assignment problem that handles a set of patients to be assigned to a set of beds over a given time horizon while taking into account availability constraints expressed in terms of beds, medical necessity, and patients demands, which are subject to modification over time. To deal with this problem, a decision support system (DSS) is developed to assist the hospital staff in the assignment activity, based on the results of a new hybrid evolutionary approach that combines the genetic algorithm with efficient evolutionary techniques and other methods from the literature. We show, with a true deep experimental study, the effectiveness of our approximate approach to solve several benchmark instances reported in the literature related to the smart health-care system. Our hybrid algorithm also outperforms efficient methods from the literature which have the previously best known results

A vector evaluated evolutionary algorithm with exploitation reinforcement for the dynamic pollution routing problem

In this paper, we investigate the Pollution Routing Problem in dynamic environments (DPRP). It consists in determining the routing plan of a fleet of vehicles supplying a set of customers, while minimizing the traveled distance and $CO_2$ emissions. The dynamic character of the problem is manifested by the occurrence of new customer demands when the working plan is in progress. Consequently, the planned routes have to be adapted in real time to include the locations of the new customers. In order to efficiently manage the trade-off between the two considered objectives, a new vector evaluated evolutionary algorithm augmented with an exploitation phase and hyper-mutation is proposed. This combination aims to reinforce the refinement of compromised solutions, and to speed up adaptation after the occurrence of a change in the problem inputs. An experimental study is conducted to test the proposed approaches on mono-objective and bi-objective test problems, and against well known approaches from the literature. The obtained results show that our proposal performs well and is highly competitive compared with the competing meta-heuristics

A multi-compartment VRP model for the health care waste transportation problem

International audienc