Efficient design and management of water distribution networks is critical for conservation of water resources and minimization of both energy requirements and maintenance costs. Several computational routines have be...
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Efficient design and management of water distribution networks is critical for conservation of water resources and minimization of both energy requirements and maintenance costs. Several computational routines have been proposed for the optimization of operational parameters that govern such networks. In particular, multi-objective evolutionary algorithms have proven to be useful both properly describing a network and optimizing its performance. Despite these computational advances, practical implementation of multi-objective optimization algorithms for water networks is an abstruse subject for researchers and engineers, particularly since efficient coupling between multi-objectivealgorithms and the hydraulic network model is required. Further, even if the coupling is successfully implemented, selecting the proper set of multi-objectivealgorithms for a given network, and addressing the quality of the obtained results (i.e., the approximate Pareto frontier) introduces additional complexities that further hinder the practical application of these algorithms. Here, we present an open-source project that couples the EPANET hydraulic network model with the jMetal framework for multi-objective optimization, allowing flexible implementation and comparison of different metaheuristic optimization algorithms through statistical quality assessment. Advantages of this project are discussed by comparing the performance of different multi-objectivealgorithms (i.e., NSGA-II, SPEA2, SMPSO) on case study water pump networks available in the literature.
A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the differences between MOEAs from algorithmic and implementation...
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ISBN:
(纸本)9783319775388
A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the differences between MOEAs from algorithmic and implementation perspective with respect to the incorporation of CHTs. To verify the effectiveness of the proposed framework, the performances of the combined algorithms of five CHTs and four MOEAs on eight constrained multi-objective optimization problems are investigated with the proposed framework. The experimental results show that the outperforming CHT can vary by constrained multi-objective optimization problems, as far as examined in this study.
A new approach in multi-objectiveevolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionarymultiobjective optimization, has not been widely...
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ISBN:
(纸本)9781538649169
A new approach in multi-objectiveevolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionarymultiobjective optimization, has not been widely used. Using this method, a multi-objective optimization problem is converted into a number of scalar subproblems, and all the subproblems are simultaneously optimized. In this paper, the performance and efficiency of the algorithm MOEA/D (multi-objectiveevolutionary algorithm based on decomposition) with the performance of two algorithms NSCA-II and MOPSO (evolution optimization methods based on dominance) for solving constrained portfolio optimization in Tehran Stock Exchange, has been compared. Portfolio return and its risk, has been considered as the optimization objectives and CvaR has been considered as a risk measure. The results indicate the high potential of these algorithms for constrained portfolio optimization. Also, the results indicate that the optimization algorithm based on decomposition has lower computational complexity, and Pareto front is more extensive than the other two methods.
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.
Aiming at reducing the large computation cost of traditional EM-driven antenna design methods, surrogate models based on back propagation neural networks (BPNN) are studied. In order to solve the problem of easily fal...
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ISBN:
(纸本)9781538673027
Aiming at reducing the large computation cost of traditional EM-driven antenna design methods, surrogate models based on back propagation neural networks (BPNN) are studied. In order to solve the problem of easily falling into local optimum in BPNN, a PSO-BPNN surrogate model is developed by improving initial structural parameters of neural networks and applied to fast multi-objective optimization design of multi-parameter antenna structures. Design results show that the proposed PSO-BPNN surrogate model can be integrating into multi-objective evolutionary algorithms for dealing with complex antenna designs with high-dimensional parameter space.
In this paper, we are interested in selection mechanisms based on the hypervolume indicator with a particular emphasis on the mechanism used in an improved version of the S metric selection evolutionarymulti-objectiv...
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In this paper, we are interested in selection mechanisms based on the hypervolume indicator with a particular emphasis on the mechanism used in an improved version of the S metric selection evolutionarymulti-objective algorithm (SMS-EMOA) called iSMS-EMOA, which exploits the locality property of the hypervolume. Here, we propose a new selection scheme which approximates the contribution of solutions to the hypervolume and it is designed to preserve the locality property exploited by iSMS-EMOA. This approach is proposed as an alternative to the use of exact hypervolume calculations and is aimed for solving many-objective optimization problems. The proposed approach is called "approximate version of the improved SMS-EMOA (aviSMS-EMOA)" and is validated using standard test problems (with three or more objectives) and performance indicators taken from the specialized literature. Our preliminary results indicate that our proposed approach is a good alternative to solve many-objective optimization problems, if we consider both quality in the solutions and running time required to obtain them because it outperforms two versions of the original SMS-EMOA that approximate the contributions to the hypervolume, it outperforms MOEA/D using penalty boundary intersection and it is competitive with respect to the original SMS-EMOA in several of the test problems adopted. Also, its computational cost is reasonable (it is slower than MOEA/D, but faster than SMS-EMOA).
Software engineering and development is well-known to suffer from unplanned overtime, which causes stress and illness in engineers and can lead to poor quality software with higher defects. Recently, we introduced a m...
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Software engineering and development is well-known to suffer from unplanned overtime, which causes stress and illness in engineers and can lead to poor quality software with higher defects. Recently, we introduced a multi-objective decision support approach to help balance project risks and duration against overtime, so that software engineers can better plan overtime. This approach was empirically evaluated on six real world software projects and compared against state-of-the-art evolutionary approaches and currently used overtime strategies. The results showed that our proposal comfortably outperformed all the benchmarks considered. This paper extends our previous work by investigating adaptive multi-objective approaches to meta-heuristic operator selection, thereby extending and (as the results show) improving algorithmic performance. We also extended our empirical study to include two new real world software projects, thereby enhancing the scientific evidence for the technical performance claims made in the paper. Our new results, over all eight projects studied, showed that our adaptive algorithm outperforms the considered state of the art multi-objective approaches in 93 percent of the experiments (with large effect size). The results also confirm that our approach significantly outperforms current overtime planning practices in 100 percent of the experiments (with large effect size).
Optimizing a multi-reservoir system is complicated, since the operation of one reservoir depends on other reservoir and may also have conflicting multiple objectives. The conflicting purposes of a multi-reservoir syst...
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Optimizing a multi-reservoir system is complicated, since the operation of one reservoir depends on other reservoir and may also have conflicting multiple objectives. The conflicting purposes of a multi-reservoir system requires a systematic multi-objective study. Recently, multi-objective evolutionary algorithms (MOEAs) have been widely used for the multi-objective analysis of the reservoir systems. However, the simple MOEAs result in premature convergence and local optimal solution for complex non-linear multi-objective optimization problems. To improve the performance and maintain the diversity in the population, chaos is being combined with the evolutionaryalgorithms for optimizing complex problems. In the present study, the chaos algorithm is coupled with MOEAs such as non-dominated genetic algorithm-II (CNSGA-II) and multi-objective differential evolution algorithm (CMODE) to derive an optimal crop planning for a multi-reservoir system having intra-basin water transfer. The model is developed with the objective of maximizing the net benefits and maximizing the crop production, subject to various physical, land and water availability constraints. The resulted optimal policy is further assessed using a simulation model and its performance is evaluated using various statistical indices. It is found that CMODE has resulted in slightly higher net benefits of Rs. 1921.77 Million and 1201.55 thousand tons of crop production with an irrigation intensity of 106.29% compared to other techniques used in this study. It has also resulted in an optimal spatial and temporal intra-basin water transfer from the upstream reservoirs to the downstream reservoir. The simulation of optimal results showed that the optimal policies obtained from CMODE performed well for longer period with less irrigation deficits. All the reservoirs in the system achieved more than 95% reliability in meeting the irrigation demands and intra-basin water transfer. (C) 2017 Elsevier B.V. All rights res
A new approach in multi-objectiveevolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionarymulti-objective optimization, has not been widely...
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A new approach in multi-objectiveevolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionarymulti-objective optimization, has not been widely used. Using this method, a multi-objective optimization problem is converted into a number of scalar subproblems, and all the subproblems are simultaneously optimized. In this paper, the performance and efficiency of the algorithm MOEA/D (multi-objectiveevolutionary algorithm based on decomposition) with the performance of two algorithms NSGA-II and MOPSO (evolution optimization methods based on dominance) for solving constrained portfolio optimization in Tehran Stock Exchange, has been compared. Portfolio return and its risk has been considered as the optimization objectives and CvaR has been considered ax a risk measure. The results indicate the high potential of these algorithms for constrained portfolio optimization. Also, the results indicate that the optimization algorithm based on decomposition has lower computational complexity, and Pareto front is more extensive than the other two methods.
The generation of weight vectors is the primary step in MOEA based on decomposition and aggregation methods, affecting the diversity of the Pareto approximation and overall performance of the algorithm. The basic meth...
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ISBN:
(纸本)9781509046010
The generation of weight vectors is the primary step in MOEA based on decomposition and aggregation methods, affecting the diversity of the Pareto approximation and overall performance of the algorithm. The basic methods, following the method proposed by Scheffe, have some limitations mainly when the number of objectives increases, because the number of weight vectors and hence the population size becomes very large. In this paper, we present a new method for weight vector generation that can create an arbitrary number of weight vectors, almost equally spaced, located in a surface in the first orthant of the objective space, with free choice of norm. The proposed evolutionary algorithm is able to prevent the creation of weight vectors along the border of the orthant, which is a region that contains solutions of little interest to the decision maker. With a small modification in the proposed method it is also possible to create cones of weight vectors, useful to explore specific regions of the decision space defined by preference directions. In our experiments, different sets of weight vectors were generated, varying the number of vectors and the dimension of the space. The validation of the results was given by the mean distance of each vector to its nearest neighbor, as well as the standard deviation and the Pearson coefficient of variation for this mean value. The results indicate that the proposed method is able to produce a distribution of vectors close to a uniform distribution, with no clustering of points, being useful for guiding decomposition-based MOEA.
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