evolutionaryalgorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the proble...
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evolutionaryalgorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the problem classes of submodular optimization, which have a wide range of applications, such as maximum coverage, sparse regression, influence maximization, document summarization and sensor placement, just to name a few. Though they have provided some theoretical explanation for the general-purpose nature of EAs, the considered submodular objective functions are defined only over sets or multisets. To complement this line of research, this paper studies the problem class of maximizing monotone submodular functions over sequences, where the objective function depends on the order of items. We prove that for each kind of previously studied monotone submodular objective functions over sequences, i.e., prefix monotone submodular functions, weakly monotone and strongly submodular functions, and DAG monotone submodular functions, a simple multi-objective EA, i.e., GSEMO, can always reach or improve the best known approximation guarantee after running polynomial time in expectation. Note that these best-known approximation guarantees can be obtained only by different greedy-style algorithms before. Empirical studies on various applications, e.g., accomplishing tasks, maximizing information gain, search-and-tracking and recommender systems, show the excellent performance of the GSEMO.(c) 2022 Elsevier B.V. All rights reserved.
The decentralization of electrical power production is conducive to a more effective and harmonious use of energy resources. For this reason, photovoltaic grid-connected plants (PVGCPs) as well as other renewable ener...
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The decentralization of electrical power production is conducive to a more effective and harmonious use of energy resources. For this reason, photovoltaic grid-connected plants (PVGCPs) as well as other renewable energy sources have come into the spotlight in recent years since they improve the supply of electrical power to the grid. The optimization of PVGCP design has been previously addressed in terms of electrical losses with successful results. However. PVGCP performance can be further enhanced if other characteristics, such as power capacity, are taken into consideration. This paper focuses on the optimization of the design of photovoltaic plants with solar tracking. The research described had the following two objectives: (i) the maximization of power capacity: (ii) the minimization of electrical losses. This problem was solved with multi-objective evolutionary algorithms, which have proved to be powerful optimization techniques that are useful for a wide range of objectives. This paper focuses on the NSGA-Il and SPEA2, two well-known multi-objectivealgorithms, and describes how they were used to optimize PVGCPs. The resulting sets of solutions provide the flexibility and adaptability needed to build a PVGCP. These algorithms were thus found to be an effective tool for enhancing PVGCP performance. (C) 2014 Elsevier Ltd. All rights reserved.
Some location problems with unreliable facilities present two different objectives, one consisting of minimizing the opening and transportation costs if none of the facilities fail and another consisting of minimizing...
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Some location problems with unreliable facilities present two different objectives, one consisting of minimizing the opening and transportation costs if none of the facilities fail and another consisting of minimizing the expected transportation costs. Usually, these different targets are combined in a single objective function and the decision maker can obtain some different solutions weighting both objectives. However, if the decision maker prefers to obtain a diverse set of non-dominated optimal solutions, then such procedure would not be effective. We have designed and implemented two multi-objective evolutionary algorithms for the realibility fixed-charge location problem by exploiting the peculiarities of this problem in order to obtain sets of solutions that are properly distributed along the Pareto-optimal frontier. The computational results demonstrate the outstanding efficiency of the proposed algorithms, although they present clear differences. (C) 2019 Elsevier B.V. All rights reserved.
As the pervasiveness of social networks increases, new NP-hard related problems become interesting for the optimization community. The objective of influence maximization is to contact the largest possible number of n...
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ISBN:
(纸本)9783319558493;9783319558486
As the pervasiveness of social networks increases, new NP-hard related problems become interesting for the optimization community. The objective of influence maximization is to contact the largest possible number of nodes in a network, starting from a small set of seed nodes, and assuming a model for information propagation. This problem is of utmost practical importance for applications ranging from social studies to marketing. The influence maximization problem is typically formulated assuming that the number of the seed nodes is a parameter. Differently, in this paper, we choose to formulate it in a multi-objective fashion, considering the minimization of the number of seed nodes among the goals, and we tackle it with an evolutionary approach. As a result, we are able to identify sets of seed nodes of different size that spread influence the best, providing factual data to trade-off costs with quality of the result. The methodology is tested on two real-world case studies, using two different influence propagation models, and compared against state-of-the-art heuristic algorithms. The results show that the proposed approach is almost always able to outperform the heuristics.
This paper describes a multi-objective evolutionaiy algorithm for a typical serial production problem, in which two or more consecutive departments must schedule their internal work, each taking into account the requi...
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ISBN:
(纸本)0780379527
This paper describes a multi-objective evolutionaiy algorithm for a typical serial production problem, in which two or more consecutive departments must schedule their internal work, each taking into account the requirements of the other departments. There are various single-objective heuristics to deal with this problem, while the multi-objective formulation calls for innovative approaches. To this aim, we devise a novel evolutionary, algorithm, and compare it with two other state-of-art genetic optimizers used in similar contexts. The results obtained on both small-size problems with known Pareto-sets, and larger problems derived from industrial production of furniture confirm the effectiveness of the proposed approach.
Recommender systems are beneficial in suggesting items to users by knowing their preferences and, therefore, effectively managing the vast amount of available information. Regarding the classical systems that focus on...
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ISBN:
(纸本)9783031686498;9783031686504
Recommender systems are beneficial in suggesting items to users by knowing their preferences and, therefore, effectively managing the vast amount of available information. Regarding the classical systems that focus on accuracy, the needs of their users have changed so much that many sometimes-conflicting performance measures now have to be taken into account. Recent research has enhanced the applicability of multi-objective evolutionary algorithms in recommender systems, balancing indicators such as accuracy with other essential ones. This survey provides a listing of recent works that applied MOEAs to the problem of recommender systems and pays special attention to critical areas, such as methodological approaches, goals, datasets, and evaluation strategies. This analysis, beyond the state-of-the-art synthesis, helps in the determination of the problems that are linked to the use of MOEAs and the prospects of the development of future research. The exploration targets aiding progress and innovation in this dynamic field.
Signal decomposition techniques prove to be useful in the analysis of neural activity, as they allow for identification of supposedly distinct neuronal structures (ie., sources of activity). Applied to measurements of...
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ISBN:
(纸本)142440133X
Signal decomposition techniques prove to be useful in the analysis of neural activity, as they allow for identification of supposedly distinct neuronal structures (ie., sources of activity). Applied to measurements of brain activity in a controlled setting as well as under exposure to an external stimulus, they allow for analysis of the impact of the stimulus on those structures. The link between the stimulus and a given source can he confirmed by a classifier that is able to "predict" if a given signal was registered under one or the other condition, solely based on the components. Very often, however, statistical criteria used in traditional decomposition techniques turn out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel hybrid technique based on multi-objective evolutionary algorithms (MOEA) and rough sets (RS) that will perform decomposition in the light of the classification problem itself.
The task of load disaggregation is inherently an optimization problem. Owing to the existence of noise level and electrical interference from neighboring systems, the real operating state of appliances is not the opti...
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The task of load disaggregation is inherently an optimization problem. Owing to the existence of noise level and electrical interference from neighboring systems, the real operating state of appliances is not the optimal solution for a single-objective function. However, most recent works weigh objective functions into a single one to construct an aggregate objective function to solve, and the weighted parameters for the different objective functions are sensitive to different datasets and are difficult to tune. Only using load data of appliances running individually to model, proposed method can identify several appliances with multiple operating modes operating simultaneously. A multi-objective load disaggregation model integrates more features including macroscopic features and microscopic features which help model to describe appliances from multiple perspectives. Five objective functions using active power, apparent power, reactive power, current waveform, and harmonics as load signatures are established to identify several electrical appliances. Proposed framework using multi-objective evolutionary algorithms for load disaggregation not only avoid adjusting weighted parameters, but also consider conflict among objectives. A problem-specific method during initialization is presented to deal with the problem that one type of appliance only works on one of these operating modes for a moment. To deal with the constraint on the number of appliances operating simultaneously, objective-rank assignment is applied. The load disaggregation is finally solved as a multi-objective problem by multi-objective evolutionary algorithms. Experimental results demonstrate the effectiveness of the proposed method for load disaggregation. The use of multi-feature methods significantly outperforms the methods using any single or two load signatures.
作者：
Ahmadi, ArasUniv Toulouse
INSA UPS INPLISBP 135 Ave Rangueil F-31077 Toulouse France INRA
UMR792 Lab Ingn Syst Biol & Proc F-31400 Toulouse France CNRS
UMR5504 F-31400 Toulouse France
A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended to provide a better accuracy/speed ratio in the convergence of multi-objective evolutionary algorithms (MOEAs) is pre...
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A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended to provide a better accuracy/speed ratio in the convergence of multi-objective evolutionary algorithms (MOEAs) is presented in this work. This algorithm works by performing an adaptive-probabilistic refinement of the search space, with no aggregation in objective space. This work investigated the integration of MAP within the state-of-the-art fast and elitist non-dominated sorting genetic algorithm (NSGAII). Considerable improvements in convergence were achieved, in terms of both speed and accuracy. Results are provided for several commonly used constrained and unconstrained benchmark problems, and comparisons are made with standalone NSGAII and hybrid NSGAII-efficient local search (eLS). (C) 2016 Elsevier B.V. All rights reserved.
Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolution...
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Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity depends on finding non-dominated fronts, this paper introduces a new method for ranking the solutions of an evolutionary algorithm's population. First, we investigate the relation between the convex hull and non-dominated solutions, and discuss the complexity time of the convex hull and non-dominated sorting problems. Then, we use convex hull concepts to present a new ranking procedure for multi-objective evolutionary algorithms. The proposed algorithm is very suitable for convex multi-objective optimization problems. Finally, we apply this method as an alternative ranking procedure to NSGA-II for non-dominated comparisons, and test it using some benchmark problems. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
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