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Provides an abstract for each of the four plenary presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
In this paper, we present results on the automatic tuning of an adaptive traffic light controller. The traffic light controller is inspired by some swarm intelligence techniques and uses numerical values that are adapted by the principles of stigmergy to estimate queue lengths. These estimates are used in a probabilistic mechanism that switches between traffic light control strategies and determines...
Provides an abstract for each of the 25 tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
This paper proposes a hybrid iterated greedy (HIG) algorithm to solve the distributed no-wait flow shop scheduling problem (DNWFSP) with the makespan criterion. The HIG mainly consists of four components, i.e. initialization phase, construction and destruction, local search, acceptance criterion. In the initialization phase, a modified NEH (Nawaz-Enscore-Ham) is proposed to generate a promising initial...
The design of robust networked structures is of significance in reality, and the integrity of network connections has been greatly emphasized in previous studies. However, besides structural integrity, a system should also keep the functionality when suffering from attacks and failures, i.e. robust community structure. Focusing on enhancing community robustness on complex networks, in this paper,...
Existing studies indicated that it is crucial to design network structures with well tolerance against potential attacks and failures in reality, and several attack models have been proposed and lucubrated. Aiming at enhancing network robustness suffering from edge-based attack cascading failures, we first propose a measure, Rce, to numerically evaluate the robustness of networks under cascading failures,...
The performance of a method for reducing the fuel consumption of a heavy duty vehicle (HDV) is described and evaluated both in simulation and using a real HDV. The method, which involves speed profile optimization using a genetic algorithm, was applied to a set of road profiles (covering sections of 10 km), resulting in average fuel savings of 11.5% and 10.2% (relative to standard cruise control),...
Due to undeniable environmental, economical and technical reasons, renewable energy-based power generation in electric power systems is continually increasing. Among renewables, photovoltaic (PV) power generation is a viable and attractive choice. For modeling photovoltaic systems, accurate modeling of PV cells is a must. PV cells are often modeled as single diode or double diode models. The process...
With the frequent occurrence of large-scale disasters, such as landslide and earthquake, timely and effective emergency resource scheduling becomes more and more important. Lots of disasters need multi-period rescue to satisfy the demand of disaster areas. In order to find a better plan to achieve the multi-period disaster relief, in this paper, a multi-period emergency resource scheduling problem...
The emergency logistics scheduling (ELS) is to enable the dispatch of emergency supplies to the victims of disasters timely and effectively, which plays a crucial role in large-scale disaster relief. In this paper, we first design a new multi-objective model that considers both the total unsatisfied time and transportation cost for the ELS problem in large-scale disaster relief (ELSP-LDR), which is...
Computing evolutionary distances using gene order data is a complex combinatory problem; nevertheless, for specific metrics exact polynomial algorithms were proposed, having in many cases non trivial approaches. This scenario can become harder if we want to reconstruct phylogenies based on gene order data: first it is necessary to explore the search space of possible tree structures which is well-known...
The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature-based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based...
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