Claudia Gómez

Verifying the Effectiveness of an Evolutionary Approach in Solving Many-Objective Optimization Problems

Most approaches in the evolutionary multi-objective optimization were found to be vulnerable in solving many-objective optimization problems (four or more objectives). This is mainly due to the fact that these algorithms lack from ability to handle more than three objectives adequately. For this reason, researchers have been focusing in developing algorithms capable of addressing many-objective optimization problems. Recently, authors of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have proposed an extension of this approach, called NSGA-III. This new approach has opened new directions for research and development to solve many-objective optimization problems. In this algorithm the maintenance of diversity among population members is aided by supplying a number of well-spread reference points. In this work, a comparative study of the performance of NSGA-II and NSGA-III was carried out. Our aim is to verify the effectiveness of NSGA-III to deal with many-objectives problems and extend the range of problems that this approach can solve. For this, the comparison was made addressing the project portfolio problem, using instances with three and nine objectives.

Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach

AbstractIn this paper, a good portfolio is found through an ant colony algorithm (including a local search) that approximates the Pareto front regarding some kind of project categorization, cardinalities, discrepancies with priorities given by the ranking, and the average rank of supported projects; this approach is an improvement towards a proper modeling of preferences. The available information is only projects’ ranking and costs, and usually, resource allocation follows the ranking priorities until they are depleted. Results show that our proposal outperforms previous approaches.

Handling of Synergy into an Algorithm for Project Portfolio Selection

Public and private organizations continuously invest on projects. With a number of candidate projects bigger than those ones that can be funded, the organization faces the problem of selecting a portfolio of projects that maximizes the expected benefits. The selection is made on the evaluation of project groups and not on the evaluation of single projects. However, there is a factor that must be taken account, since it can significantly change the evaluation of groups: synergy. This is that two or more projects are complemented in a way that generates an additional benefit to they already own individually. Redundancy, a special case of synergy, occurs when two or more projects cannot be financed simultaneously. Both features add complexity to the evaluation of project groups. This article presents an evaluation of the two most used alternatives for handling synergy, in order to incorporate it into an ant-colony metaheuristic for solving project portfolio selection.

An Ant Colony Algorithm for Solving the Selection Portfolio Problem, Using a Quality-Assessment Model for Portfolios of Projects Expressed by a Priority Ranking

One of the most important problems faced by any organization is make decisions about how to invest and manage the resources to get more benefits; however, the organizations resources are not enough to support all portfolios proposals. To these problems that face the executives of the big organizations, is known as Select Portfolio Problem. In this work is developed an ant colony algorithm, which is an especially effective meta-heuristic, this meta-heuristic is hybridized with a multi-objective local search, this strategy allows using knowledge of the ant, to build potential solutions, knowledge is obtained through the pheromone trail left by ants when find good solutions, for that the algorithm does not converge prematurely an evaporation strategy is implemented. The strategy meta-heuristic include an optimization model for portfolio selection called discrepancies model, this model is implemented when the information concerned to the quality of the projects is in form of ranking, besides help to evaluate portfolios through ten criteria to maximize the impact of the portfolio. This approach allowed reaching privileged areas of Pareto’s front, where identified solutions that reflect the preferences of the decision maker. The experimental tests show the advantages of our proposal, providing reasonable evidence of its potential for solving the select portfolio problems with many objectives.

Preference Incorporation into Evolutionary Multiobjective Optimization Using a Multi-Criteria Evaluation Method

Most approaches in the evolutionary multiobjective optimization literature concentrate mainly on generating an approximation of the Pareto front. However, this does not completely solve the problem since the Decision Maker (DM) still has to choose the best compromise solution out of that set. This task becomes difficult when the number of criteria increases. In this chapter, we introduce a new way to incorporate and update the DM’s preferences into a Multiobjective Evolutionary Algorithm, expressed in a set of solutions assigned to ordered categories. We propose a variant of the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II), called Hybrid-MultiCriteria Sorting Genetic Algorithm (H-MCSGA). In this algorithm, we strengthen the selective pressure based on dominance adding selective pressure based on assignments to categories. Particularly, we make selective pressure towards non-dominated solutions that belong to the best category. In instances with 9 objectives on the project portfolio problem, H-MCSGA outperforms NSGA-II obtaining non-dominated solutions that belong to the most preferred category.

Project Ranking-Based Portfolio Selection Using Evolutionary Multiobjective Optimization of a Vector Proxy Impact Measure

Selecting project portfolios Decision-Maker usually starts with limited information about projects and portfolios. One of the challenges involved in analyzing, searching and selecting the best portfolio is having a method to evaluate the impact of every project and portfolio in order to compare them. This paper develops a model for composing publicoriented project portfolios. Information concerning the quality of the projects is in the form of a project-ranking, which can be obtained by the application of a proper multi-criteria method; however the ranking does not assume an appropriate evaluation. A best portfolio is primarily found through a multi-objective optimization that regards the impact indicators that reflect the quality of the projects in the portfolio and competent portfolios’ cardinalities. Overall good solutions are obtained by developing an evolutionary method, which is found to perform well in some test examples.

Hyperheuristic for the parameter tuning of a bio-inspired algorithm of query routing in p2p networks

The computational optimization field defines the parameter tuning problem as the correct selection of the parameter values in order to stabilize the behavior of the algorithms. This paper deals the parameters tuning in dynamic and large-scale conditions for an algorithm that solves the Semantic Query Routing Problem (SQRP) in peer-to-peer networks. In order to solve SQRP, the HH_AdaNAS algorithm is proposed, which is an ant colony algorithm that deals synchronously with two processes. The first process consists in generating a SQRP solution. The second one, on the other hand, has the goal to adjust the Time To Live parameter of each ant, through a hyperheuristic. HH_AdaNAS performs adaptive control through the hyperheuristic considering SQRP local conditions. The experimental results show that HH_AdaNAS, incorporating the techniques of parameters tuning with hyperheuristics, increases its performance by 2.42% compared with the algorithms to solve SQRP found in literature.

Evaluation of the Evolutionary Algorithms Performance in Many-Objective Optimization Problems Using Quality Indicators

The need to address more complex real-world problems gives rise to new research issues in many-objective optimization field. Recently, researchers have focused in developing algorithms able to solve optimization problems with more than three objectives known as many-objective optimization problems. Some methodologies have been developed into the context of this kind of problems, such as A2-NSGA-III that is an adaptive extension of the well-known NSGA-II (Non-dominated Sorting Genetic Algorithm II). A2-NSGA-III was developed for promoting a better spreading of the solutions in the Pareto front using an improved approach based on reference points. In this paper, a comparative study between NSGA-II and A2-NSGA-III is presented. We examine the performance of both algorithms by applying them to the project portfolio problem with 9 and 16 objectives. Our purpose is to validate the effectiveness of A2-NSGA-III to deal with many-objective problems and increase the variety of problems that this method can solve. Several quality indicators were used to measure the performance of the two algorithms.

Multicriteria optimization of interdependent project portfolios with 'a priori' incorporation of decision maker preferences

One of the most important management issues lies in determining the best portfolio of a given set of investment proposals. This decision involves the pursuit of multiple criteria, and has been commonly addressed by implementing a two-phase procedure whose first step identifies the efficient solution space. In this paper we introduce our algorithm called Non-Outranked Ant Colony Optimization (NO-ACO) that optimizes portfolios with interprojects interactions whilst takes into account the DM’s preferences by incorporating a priori preferences articulation. Experimental tests show the advantages of our proposal over the two-phase approach. Also, NO-ACO performed particularly well for problems with high dimensionality.

New Implementations of Data Mining in a Plethora of Human Activities

The fast growth of the societies along with the development and use of the technology, due to this at the moment have much information which can be analyzed in the search of relevant informationto make predictions or decision making. Knowledge Discovery and Data Mining are powerful data analysis tools. The term Data mining is used to describe the non-trivial extraction of implicit, Data Mining is a discovery process in large and complex data set, refers to extracting knowledge from data bases. Data mining is a multidisciplinary field with many techniques. Whit this techniques you can create a mining model that describe the data that you will use (Ponce et al., 2009a). Typical Data Mining techniques include clustering, association rule mining, classification, and regression. We show an overview of some algorithms that used the data mining to solve problems that arisen from the human activities like: Electrical Power Design, Trash Collectors Routes, Frauds in Saving Houses, Vehicle Routing Problem. One of the reasons why the Data Mining techniques are widely used is that there is a need to transform a large amount of data on information and knowledge useful. Having a large amount of data and not have tools that can process a phenomenon has been described as rich in data but poverty in information (Han & Kamber, 2006). This steady growth of data, which is stored in large databases, has exceeded the ability of human beings to understand. Moreover, various problems they might present a constant stream of data, which may be more difficult to analyze the power of information.