As one of the most challenging combinatorial optimization problems in scheduling, the resource-constrained project scheduling problem (RCPSP) has attracted numerous scholars' interest resulting in considerable res...
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ISBN:
(纸本)9781450326629
As one of the most challenging combinatorial optimization problems in scheduling, the resource-constrained project scheduling problem (RCPSP) has attracted numerous scholars' interest resulting in considerable research in the past few decades. However, most of these papers focused on the single objective RCPSP;only a few papers concentrated on the multi-objective resource-constrained project scheduling problem (MORCPSP). Inspired by a procedure called electromagnetism (EM), which can help a generic populationbased evolutionary search algorithm to obtain good results for single objective RCPSP, in this paper we attempt to extend EM and hybridize it with three reputable state-of-theart multi-objective evolutionary algorithms (M0EAs) i.e. NSGA-II, SPEA2 and MOEA/D, for MORCPSP. Our two objectives are minimizing makespan and total tardiness. We perform computational experiments on standard benchmark datasets. Empirical comparison and analysis of the results obtained by the hybridization versions of EM with NSGA-II, SPEA2 and MOEA/D are conducted. The results demonstrate that EM can improve the performance of NSGA-II and SPEA2.
Dynamic constrained multi-objective optimization problems (DCMOPs) involve objectives, constraints, and parameters that change over time. This kind of problem presents a greater challenge for evolutionary algorithms b...
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Dynamic constrained multi-objective optimization problems (DCMOPs) involve objectives, constraints, and parameters that change over time. This kind of problem presents a greater challenge for evolutionary algorithms because it requires the population to quickly track the changing pareto-optimal set (PS) under constrained conditions while maintaining the feasibility and good distribution of the population. To address these challenges, this paper proposes a dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement (CEDE), in which we have made improvements to both the static optimization and dynamic response parts, innovatively utilizing the valuable information latent in the optimization process to help the population evolve more comprehensively. The static optimization involves the co-evolution of three populations, through which their mutual synergy can more comprehensively identify potential true PS and provide more useful historical information for dynamic response. Additionally, to prevent the elimination of potentially valuable infeasible individuals (i.e., individuals that are not dominated by feasible individuals) due to pareto domination, we employ an archive set to store and update these individuals. When the environment changes, to effectively enhance population diversity under complex dynamic constraints and help the population to respond quickly to changes, we propose a diversity enhancement strategy, which includes a diversity maintenance strategy and a center point-based exploration strategy. This strategy effectively enhances population diversity in complex and changing environments, helping the population respond quickly to changes. The effectiveness of the algorithm is validated through two test sets. The experimental results show that CEDE can effectively use valuable information to cope with complex dynamic constraint environments. Compared with several of the most advanced algorithms, it is superio
evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed Negatively Correlated Search (NCS) pr...
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evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed Negatively Correlated Search (NCS) provides a distinct parallel exploration search behavior and is expected to facilitate RL more effectively. Considering that the commonly adopted neural policies usually involves millions of parameters to be optimized, the direct application of NCS to RL may face a great challenge of the large-scale search space. To address this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC) framework to scale-up NCS while largely preserving its parallel exploration search behavior. The issue of traditional CC that can deteriorate NCS is also discussed. Empirical studies on 10 popular Atari games show that the proposed method can significantly outperform three state-ofthe-art deep RL methods with 50% less computational time by effectively exploring a 1.7 million-dimensional search space.
People nowadays deal with busy and dynamic lifestyles on a daily basis. Adopting or maintaining a healthy lifestyle to prevent chronic conditions is therefore a core societal challenge. It is thus critical to engage a...
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People nowadays deal with busy and dynamic lifestyles on a daily basis. Adopting or maintaining a healthy lifestyle to prevent chronic conditions is therefore a core societal challenge. It is thus critical to engage and motivate citizens with healthy and tailored activities that they like, as a key driver for safeguarding good health from a preventive vantage point, aligned with the pursuance of SDG 3: "good health and well-being". This is why Health Recommender Systems have recently become a research trend, particularly in the domains of food and physical activity recommendation. In this work, we present F-EvoRecSys: an extension of an evolutionary algorithm-driven solution for "healthy bundle" recommendations to help users improve their well-being. F-EvoRecSys presents the novelty of incorporating a fuzzy inference system with the aim of improving physical activity recommendations, predicated on users' exercising habit information. Through an experimental study and a live study with real participants, we demonstrate the feasibility of F-EvoRecSys to produce more diversified recommendations, while maintaing a balance between adapting to the user health needs and matching her/his individual preferences. We finally provide a discussion about challenges and future directions for personalized well-being recommender systems, under three points of view: AI and data approaches, role of fuzzy systems, and application domain considerations.
We consider a ratio -based efficiency analysis in view of achieving performance targets by Decision Making Units. The targets refer to the efficiency scores or ranks and the truth of pairwise preference relations. The...
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We consider a ratio -based efficiency analysis in view of achieving performance targets by Decision Making Units. The targets refer to the efficiency scores or ranks and the truth of pairwise preference relations. These outcomes can be instantiated for at least one or all feasible weights associated with inputs and outputs. We discuss Multiple Objective Optimization problems that minimize the required reductions of consumed resources or increases of produced results needed to reach the pre -defined aims. The proposed models offer great flexibility to the Decision Maker, who can indicate which factors should be modified and to which extent. The evolutionary optimization algorithm computes a set of solutions that exhibit the trade-offs between expected modifications of various factors. They can serve as the basis for interactively selecting the most preferred solution to be implemented in practice. The use of the proposed framework is illustrated in the problem of analyzing the efficiency of Polish airports.
It is witnessed that the popularity of the research in cybersecurity using bio-inspired algorithms (a key subset of natural algorithms) is ever-growing. As an emergent research area, researchers have devoted efforts t...
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It is witnessed that the popularity of the research in cybersecurity using bio-inspired algorithms (a key subset of natural algorithms) is ever-growing. As an emergent research area, researchers have devoted efforts to applying and comparing various bio-inspired algorithms to cybersecurity applications. It is necessary to have a systematic review of bio-inspired algorithms for cybersecurity to fill the gap in the missing research study on this topic. The research contributions of this review article are four-fold. It first highlights the foundation of the baseline and latest development of 12 popular bio-inspired algorithms in three categories namely ecology-based, evolutionary-based and swarm intelligence-based algorithms. A systematic review is conducted to synthesise and compare the research methodologies, results and limitations. In-depth discussion will be made on the shortlisted and highly cited articles. The tips to select appropriate algorithm or the combination of multiple algorithms have been reported, along with the pros and cons on the design and formulations. Future research directions will be presented to meet the trends and unexplored research.
We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multiobjective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon prelimi...
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We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multiobjective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyze the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a *** prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the sub-graph-based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front.
The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implement...
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The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implemented in many popular programming languages. However, the algorithm has continued to evolve, with newer, improved variants often achieving superior results over the original. Unfortunately, many of these modifications are not readily accessible as prebuilt programming solutions, creating a need for a comprehensive programming library that includes the most popular and effective variants of the base algorithm. The library we designed, DetPy (Differential Evolution Tools), provides implementations of the standard differential evolution algorithm along with 15 distinct variants. This tool allows researchers working on optimization problems to compare multiple algorithmic approaches, making it easier to select the most effective solution for their specific challenges.
Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly de...
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Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.
This study introduces an interactive evolutionary algorithm (EA) for optimizing path planning in groundfish surveys. The approach employs interactive reoptimization to iteratively refine plans by adjusting constraints...
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