Real world systems such as advanced energy systems and smart grids are becoming more complex and interconnected. A critical issue in such systems is to ensure that the interactions of a large number of components do n...
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Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalit...
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Many proteins involved in human proteinopathies exhibit complex energy landscapes with multiple thermo dynamically-stable and semi-stable structural states. Landscape reconstruction is crucial to understanding functio...
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Parallelization of fitness evaluation is an established practice in evolutionary computation, and is a necessity in applications where fitness functions are computationally expensive. Traditional master-slave EAs base...
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Interest in co-evolutionary algorithms was triggered in part with Hillis 1991 paper describing his success in using one to evolve sorting networks. Since then there have been heightened expectations for using this nat...
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evolutionary multi-objective optimization (EMO) algorithms are widely used to solve problems involving multiple conflicting objectives. In general, these problems result in a well-distributed and diverse set of Pareto...
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ISBN:
(纸本)9798400704949
evolutionary multi-objective optimization (EMO) algorithms are widely used to solve problems involving multiple conflicting objectives. In general, these problems result in a well-distributed and diverse set of Pareto-optimal solutions, consisting of individual objective-optimal solutions at their extreme and various compromise objective solutions at their core. However, in practice, decision-makers (DMs) usually have certain pre-conceived preference information which may make a majority of the Pareto solution set uninteresting to the DMs. In such cases, DM's preference information can be utilized to update EMO algorithms to focus on the preferred part of the Pareto set, rather than the entire Pareto set. While EMO researchers have proposed preference-based EMO algorithms for this purpose, appropriate metrics to evaluate their performance have received lukewarm attention. In this paper, we critically analyze a recently proposed preference-based hypervolume (R-HV) metric for its sensitivity to handle various scenarios and propose an updated version to remedy the difficulties associated with it. The updated R-HV metric is then compared with the original R-HV metric on solutions obtained from a number of preference-based EMO algorithms. The suggestion of a more appropriate R-HV metric presented in this paper should encourage further research in preference-based multi-objective optimization.
Maintenance and Rehabilitation (M&R) scheduling is one of the vital aspects of a pavement management system (PMS). This study aims to establish accurate M&R plans for a large-scale pavement network. To this in...
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Maintenance and Rehabilitation (M&R) scheduling is one of the vital aspects of a pavement management system (PMS). This study aims to establish accurate M&R plans for a large-scale pavement network. To this intent, parameters affecting pavement deterioration were identified from the literature, then Random Forest Regression was employed to determine the effective features for pavement deterioration modelling. An accurate pavement deterioration function was generated by applying significant features. The most robust metaheuristic and evolutionary algorithms were selected and adjusted to solve the M&R scheduling optimisation problem, including the Water Cycle Algorithm (WCA), Arithmetic Optimisation Algorithm (AOA), Differential evolutionary (DE), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Genetic Algorithm (GA). The performance of the mentioned algorithms was compared to help researchers and decision-makers to select the appropriate algorithm for M&R scheduling optimisation. WCA and AOA showed to have the best performance among the adapted algorithms. Compared to AOA, DE, ACO, PSO, and GA, WCA's objective function was calculated to be 45%, 74%, 74%, 77% and 83% less, while its M&R cost was cheaper by 13%, 16%, 27%, 19%, and 18%, respectively.
The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has em...
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The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts. (C) 2014 Elsevier Ltd. All rights reserved.
Multi-robot path planning has evolved from research to real applications in warehouses and other domains;the knowledge on this topic is reflected in the large amount of related research published in recent years on in...
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Multi-robot path planning has evolved from research to real applications in warehouses and other domains;the knowledge on this topic is reflected in the large amount of related research published in recent years on international journals. The main focus of existing research relates to the generation of efficient routes, relying the collision detection to the local sensory system and creating a solution based on local search methods. This approach implies the robots having a good sensory system and also the computation capabilities to take decisions on the fly. In some controlled environments, such as virtual labs or industrial plants, these restrictions overtake the actual needs as simpler robots are sufficient. Therefore, the multi-robot path planning must solve the collisions beforehand. This study focuses on the generation of efficient collision-free multi-robot path planning solutions for such controlled environments, extending our previous research. The proposal combines the optimization capabilities of the A* algorithm with the search capabilities of co-evolutionary algorithms. The outcome is a set of routes, either from A* or from the co-evolutionary process, that are collision-free;this set is generated in real-time and makes its implementation on edge-computing devices feasible. Although further research is needed to reduce the computational time, the computational experiments performed in this study confirm a good performance of the proposed approach in solving complex cases where well-known alternatives, such as M* or WHCA, fail in finding suitable solutions.
Through constraints, declarative process models represent the permitted behaviour associated with a business process, by limiting the potential correct traces. These models can be discovered by analysing an event log....
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ISBN:
(纸本)9783031345593;9783031345609
Through constraints, declarative process models represent the permitted behaviour associated with a business process, by limiting the potential correct traces. These models can be discovered by analysing an event log. However, various declarative business models can be extracted from a single event log, depending on the desirable level of metrics, such as fitness and generalisation. Existing discovery algorithms enable the type of discovered declarative process model to be customised through a set of configuration parameters. Depending on the values of these parameters, the discovered process can be of high or low quality. Unfortunately, the high number of combinatorial parameters and the high time consumption of process discovery make it impractical to conduct an exhaustive analysis of the configuration parameters to determine the most suitable declarative process model discovered. As a solution, we propose a methodology supported by an implemented framework that uses evolutionary algorithms to reduce computational complexity and to select the highest quality declarative business processes. An experiment is included to show the feasibility of our proposal.
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