ABSTRACT Multicriteria decision‐making (MCDM) often involves evaluating or ranking alternatives on multiple attributes, a process that is far from trivial due to flexible preferences and uncertainty in the criteria importance. The recently proposed Weightless, Interval‐Based Approach (WIBA) tackles these issues by relying on an ordering of the criteria (according to their relevance) instead of explicit weights and using interval scores to evaluate alternatives. Although originally proposed for selecting solutions of interest in the context of multi‐objective and many objective optimization problems, it can be adapted to rank such solutions. However, the robustness of WIBA rankings has not been studied, and sensitivity analysis approaches based on perturbations of the weights cannot be applied. Furthermore, there is no friendly environment for exploring WIBA properties. This paper addresses these gaps by (1) introducing a novel local sensitivity analysis technique to explore how small perturbations in the order of criteria affect rankings, and (2) presenting WIBApp, a freely available visual software tool that implements WIBA features, including the proposed sensitivity analysis. Using a case study on the selection of technical universities, the paper first illustrates WIBA's flexibility and utility in real‐world decision scenarios, enabling decision makers to effectively deal with uncertainty and complexity, and second shows how WIBApp simplifies data management, enhances analysis and facilitates comparisons among rankings. By advancing the theoretical foundations of WIBA and providing a practical implementation, this work contributes to providing decision makers with a robust framework for handling multi‐criteria problems, enhancing the reliability of rankings and supporting informed decisions.

Decisions made in areas such as economics, engineering, industry, and medical sciences are usually based on finding and interpreting solutions to optimization problems. When modeling an optimization problem, it should be clear that people do not make decisions in a vacuum or in isolation from the reality. So, there is always a decision-making context that, in addition to thenaturalconstraints of the problem, acts as a filter on the candidate solutions available. If this fact is omitted, optimal but useless solutions to the problem can be obtained. In this article, we propose a systematic way of modeling contexts based on fuzzy propositions and two approaches (a priori and a posteriori) for solving optimization problems under their influence. In the proposed a priori approach, the context is explicitly included in the mathematical model of the problem. As this approach may have a limited application due to the increasing number of constraints and their nature, an a posteriori approach is proposed, in which a set of solutions, obtained by any means (like exact algorithms, simulation, or metaheuristics), are checked for their suitability to the context by using a multicriteria decision-making methodology. A simple fish harvesting problem in a sustainability context and a tourist trip design problem in a pandemic context were solved for illustration purposes. Our results provide researchers and practitioners with a methodology for more effective optimization and decision making.

A “standard” map provides a simplified representation of the real world but is not able to adapt to the needs of different users having different preferences. On the contrary, adaptive maps are aimed at representing the world as seen through the eyes of the user depicting the suitability/difficulty of the paths between points of interest according to the user's preferences. Adaptive maps consider, simultaneously, multiple attributes like travel time, distance, slopes, etc. In this paper, a model for adaptive maps is presented. A generation and a visualization methods are proposed. To illustrate the results, five different adaptive maps are generated and visualized considering different attributes and preferences.

Robust optimization over time (ROOT) is a novel research topic in the field of evolutionary dynamic optimization, in which only just there has been few advances. Specifically, one of the less developed topic is how to define an approximation model (AM) that suitably characterizes the past environments. For a ROOT algorithm this is a key issue that affects its performance, because AMs are used for estimating robustness (e.g by predicting fitness in future environments). In this work we studied the role of several approximation models (AM) for characterizing past environments in the context of robust optimization over time (ROOT). We have conducted several computational experiments in order to analyze the effects of different AMs in the algorithm performance over continuous ROOT problems. We observed that the Support Vector Regression model with Laplacian Kernel allows the algorithm to achieve a significantly, high performance.

We focus here in the deployment of Wi-Fi hotspots (Internet access points) in several locations of Havana city, Cuba. The key point to consider is that the availability of the locations is related to the risk of sea flooding in the littoral of the city.We propose to model the problem as a Maximal Coverage Location Problem (MCLP) where the availability of locations is considered in fuzzy terms, thus leading to fuzzy constraints. Using real data from the Havana city, we solve the problem with a parametric approach using a metaheuristics portfolio. An extensive analysis of the solutions shows the benefits of the proposal.

The Personalized Route Problem with fuzzy constraints deals with the problem of finding a set of routes according to the users's preferences. The solutions are obtained solving a set of problems associated with different α-cuts. However, under different α values unique solutions are not guaranteed, being possible to obtain a set of duplicated solutions, which is not desirable.Here we propose a method to reduce the number of equal solutions focusing on solving specific α-cuts, thus avoiding other ones that will obtain a duplicated solution. Two examples are shown to illustrate the proposal.

At present, there is a figure of more than 500 thousand houses and 42 thousand classrooms of public schools without electricity in rural and indigenous zones in Mexico. This is due to the fact that they are not connected to the electricity distribution network and as a consequence are the ones that are most in need of electrification projects. Taking this problem into account, this work models and applies a Fuzzy AHP-TOPSIS multicriteria decision method to select the most appropriate electrification system in a rural area located in the south of Mexico. The AHP method is used to calculate the weights and the TOPSIS method is used to obtain the ranking of the alternatives. To carry out this study, a group of three experts in the Mexican energy field who selected a survey were selected. Data were obtained through the corresponding authorities and the families that make up the community. As a result, it was found that a photovoltaic system of 1 kWp isolated was the best alternative to supply electricity to each of the 300 families that make up the community of Cerro Hermoso.

The Personalized Route Problem resembles the so-called multi-criteria Shortest Path Problem where every arc (street) of the graph (map) has a set of costs (values) associated to a set of criteria (features). The personalization of the path comes when the user a) determines how important is each criterion according to personal preferences and b) indicates constraints on the criteria values. The preferences are often uncertain, imprecise or inexact. Also, it seems reasonable to allow some relaxation on the constraints. We propose and define a new model for the Personalized Route Problem with fuzzy constraints. As an example, we show its application to the Shortest Path Route with fuzzy constraints on the slopes values.

Given a multicriteria decision-making problem, an obvious question emerges: Which method should be used to solve it? Although some efforts had been made, the question remains open. The aim of this contribution is to compare a set of multicriteria decision-making methods sharing three features: same fuzzy information as input data, the need of a data normalization procedure, and quite similar information processing. We analyze the rankings produced by fuzzy MULTIMOORA, fuzzy TOPSIS (with two normalizations), fuzzy VIKOR, and fuzzy WASPAS with different parameterizations, over 1200 randomly generated decision problems. The results clearly show their similarities and differences, the impact of the parameters settings, and how the methods can be clustered, thus providing some guidelines for their selection and usage.

Adaptation to changes in real scenarios is not a “cost-free” operation. However, in general, this is not considered in most of the studies done in dynamic optimization problems. Our focus here is to analyse what happens when a relocation cost is added in the dynamic maximal covering location problem, that is, when the adaptation to changes entails some cost. Comparing two models with and without “cost-free” adaptation, respectively, we study how much coverage is lost, how similar are the solutions obtained for both problems along the time and we preliminarily explore the relation between solution similarity and coverage differences.

In this paper, the literature associated with the covering location problems addressing uncertainty under a fuzzy approach is reviewed. Specifically, the papers related to the most commonly applied models such as set covering location problem, maximal covering location problem, and hub covering location problem are examined. An annotated bibliography is presented in which such papers have been classified according to the following criteria: the fuzzy items considered in the proposed model, the type of problem addressed, the fuzzy approach applied, the method of resolution, and field of application considered. This research provides useful information that helps to identify some opportunities for the application of fuzzy approaches to the covering location problems.

This work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic aspect of car driving. Moreover, this modularity of the architecture will allow us to replace or add modules in the future as a way to enhance particular features of particular situations. In the present work, some of the modules are implemented by means of hand-designed driving heuristics, whereas modules responsible for adapting the speed and direction of the vehicle to the track's shape, both critical aspects of driving a vehicle, are optimized by means of a genetic algorithm that evaluates the performance of the controller in four different tracks to obtain the best controller in a large number of situations; the algorithm also penalizes controllers that go out of the track, lose control, or get damaged. The evaluation of the performance is done in two ways. First, in runs with and without adversaries over several tracks. And second, the architecture was submitted as a participant to the 2010 Simulated Car Racing Competition, which in end won laurels. © 2012 Wiley Periodicals, Inc.

Developing predictive models is one of the key issues in Systems Biology. A critical problems that arises when these models are built is the parameter estimation. The calibration of these nonlinear dynamic models is stated as a nonlinear programming problems (NLP) and its resolution is usually complex due to the frequent ill-conditioning and multimodality of the majority of these problems. For that reason, the use of hybrid stochastic optimization methods has received an increasing interest in recent years. In this work we present a new hybrid method for parameter estimation in Systems Biology. This proposal consists on a set of DE algorithms that cooperate among them through a centralised scheme in which a coordinator controls their behavior by means of a rule system. The comparison with state-of-the-art methods shows the better performance of this cooperative strategy when the complexity of the instances is increased.

Technology foresight deals with the necessity of anticipating the future to better adapt to new situations regarding innovations that directly affect business world. One widely spread methodology in technology foresight is Godet's Scenario Method, which includes a module (MICMAC) performing the so-called structural analysis. The goal of the structural analysis is to identify the most important variables in a system. To this end, it makes use of an influence matrix that describes the relations between the variables. This information is usually given by experts based on their own knowledge and experience. However, some of the information of the influence matrix may contain errors due to the subjective nature of the criteria and opinions of the experts. Here we propose a new analysis that follows a multi-objective approach and allows to measure the sensibility of the model versus possible errors at the input. The well-known NSGA-II algorithm has been used as a solver. The results are encouraging and deserve further investigation.

The Particle Swarm Optimization (PSO) algorithm has been successfully applied to dynamic optimization problems with very competitive results. One of its best performing variants, the mQSO is based on an atomic model, with quantum and trajectory particles. This work introduces a new version of this algorithm which uses heuristic rules for improving its performance. Two new rules are presented: one specifically designed for the mQSO, which locally bursts diversity after a change in the environment, and a second, more general one, which globally increases diversity in a precise way, without disturbing the intensification of the search. The new version with rules is tested against the original one using several variations of the Moving Peaks Benchmark and the Ackley function. The results show a drastic improvement in the performance of the algorithm.

Optimisation in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). The possibility to use a new centralised cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimisation Problems (DOPs) was previously introduced showing good results against state of the art methods like the Particle Swarm Optimisation (PSO) variant with multiple swarms and different types of particles. The analysis of this previous work are further extended here by exploring more possibilities for the cooperation rules used in the strategy. The results show that different classes of cooperation can lead to quite different results, some of them greatly outperforming the previous ones.

Adversarial decision making is aimed at finding strategies for dealing with an adversary who observes our decisions and tries to learn our behaviour pattern. This contribution extends a simple mathematical model with strategies that vary along time, and motivates the use of heuristic search procedures to address the problem of finding good strategies within this new search space. The evaluation of this new class of strategies requires running a stochastic simulation so the comparison of strategies should be properly addressed. A new statistics-based technique for the comparison of strategies is also proposed and tested in this context when coupled with a Genetic Algorithm. Computational experiments showed that the new strategies are better than previous ones, and that the results obtained with this new comparison technique are encouraging.

Abstract Optimization problems are ubiquitous in our daily lives and one way to cope with them is using cooperative optimization systems that allow to obtain good enough, fast enough, and cheap enough solutions. From a practical point of view, the design and the analysis of such systems are complex tasks. In this work, an integrated system (DACOS) for helping in the design and analysis of cooperative, centralized optimization systems is presented. Also, the methodology used for the creation of DACOS (mainly, the use of software modeling) is described in detail. This may also be useful for researchers who want to build up their own system for their particular needs. DACOS has been developed using the Eclipse developing framework, which, among other advantages, is also able to automatically generate source code. Finally, a practical case of use is presented: the application of DACOS to the configuration and analysis of a cooperative strategy on a location problem. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we overview the 2009 Simulated Car Racing Championship-an event comprising three competitions held in association with the 2009 IEEE Congress on Evolutionary Computation (CEC), the 2009 ACM Genetic and Evolutionary Computation Conference (GECCO), and the 2009 IEEE Symposium on Computational Intelligence and Games (CIG). First, we describe the competition regulations and the software framework. Then, the five best teams describe the methods of computational intelligence they used to develop their drivers and the lessons they learned from the participation in the championship. The organizers provide short summaries of the other competitors. Finally, we summarize the championship results, followed by a discussion about what the organizers learned about 1) the development of high-performing car racing controllers and 2) the organization of scientific competitions.

We consider a problem of mediated group decision making where a number of agents provide a preference function over a set of alternatives. Then, using such information, a new agent provides its own preferences, and, after that, a mediation step is applied to aggregate the individual preferences in order to obtain a group-preference function. Finally, the most supported alternative is selected. Two key aspects are that the preference functions of the former agents may or may not have uncertainty, and that the mediation process rewards those agents that are open to other alternatives besides their most preferred ones. The question for the new agent is how to score its alternatives in such a way that its most preferred one gets the biggest group support. We propose to define such scoring or preference function as the solution of a nonlinear optimization problem. The model also takes into account that imprecision could exist in the preference functions. Through extensive simulations (varying the number of agents, alternatives, etc.), we conclude that the proposal is feasible and effective. Additionally, the usefulness of the mediation process rewarding openness is empirically confirmed.

In recent years, biological and natural processes have been increasingly influencing methodologies in science and technology. In particular, the role played by the cooperation among individuals is being studied more frequently and profoundly in diverse areas of knowledge. We present here a multiagent decentralized strategy for dynamic optimization problems where a population of cooperative agents and solutions are used to deal with the moving peaks problem. We focus on cooperation and diversity mechanisms, and we study how different alternatives affect the performance of the strategy. © 2009 Wiley Periodicals, Inc.

BACKGROUND: Protein structure comparison is a key problem in bioinformatics. There exist several methods for doing protein comparison, being the solution of the Maximum Contact Map Overlap problem (MAX-CMO) one of the alternatives available. Although this problem may be solved using exact algorithms, researchers require approximate algorithms that obtain good quality solutions using less computational resources than the formers. RESULTS: We propose a variable neighborhood search metaheuristic for solving MAX-CMO. We analyze this strategy in two aspects: 1) from an optimization point of view the strategy is tested on two different datasets, obtaining an error of 3.5%(over 2702 pairs) and 1.7% (over 161 pairs) with respect to optimal values; thus leading to high accurate solutions in a simpler and less expensive way than exact algorithms; 2) in terms of protein structure classification, we conduct experiments on three datasets and show that is feasible to detect structural similarities at SCOP's family and CATH's architecture levels using normalized overlap values. Some limitations and the role of normalization are outlined for doing classification at SCOP's fold level. CONCLUSION: We designed, implemented and tested.a new tool for solving MAX-CMO, based on a well-known metaheuristic technique. The good balance between solution's quality and computational effort makes it a valuable tool. Moreover, to the best of our knowledge, this is the first time the MAX-CMO measure is tested at SCOP's fold and CATH's architecture levels with encouraging results.

The comparison of protein structures is an important problem in bioinformatics, and soft computing techniques were recently introduced for achieving a better representation and potentially, for getting better solving strategies. We focus here in the generalized maximum fuzzy contact map overlap model for analyzing the impact of the fuzzy contact map's definition, and the relation between the crisp and fuzzy costs. Surprisingly, we detected some situations where solving the fuzzy model gave better results in terms of crisp values than solving the crisp model directly.

In the context of optimization problems, metaheuristics are tools that stand out by its excellent results and generality. A lot of metaheuristics are formed by a population of agents that operates in a search space. A frame of metaheuristics inspired in cooperation between unrelated individuals is proposed and three different methods of cooperation are suggested. The implementation of the cooperation between agents is made using soft computing techniques. A fuzzy rules system has been designed concretely to perform the cooperation. Details about the implementation of three methods of cooperation and the computation of the fuzzy rules are offered for the models considered. A framework of experimentation over the combinations of methods and models is proposed.

Abstract Motivation: As an increasing number of protein structures become available, the need for algorithms that can quantify the similarity between protein structures increases as well. Thus, the comparison of proteins' structures, and their clustering accordingly to a given similarity measure, is at the core of today's biomedical research. In this paper, we show how an algorithmic information theory inspired Universal Similarity Metric (USM) can be used to calculate similarities between protein pairs. The method, besides being theoretically supported, is surprisingly simple to implement and computationally efficient. Results: Structural similarity between proteins in four different datasets was measured using the USM. The sample employed represented alpha, beta, alpha–beta, tim–barrel, globins and serpine protein types. The use of the proposed metric allows for a correct measurement of similarity and classification of the proteins in the four datasets. Availability: All the scripts and programs used for the preparation of this paper are available at http://www.cs.nott.ac.uk/~nxk/USM/protocol.html. In that web-page the reader will find a brief description on how to use the various scripts and programs. Supplementary information: The protein datasets used are collected in http://www.cs.nott.ac.uk/~nxk/USM/datasets.html. The calculated similarity values for the proteins used in this paper can be found in http://www.cs.nott.ac.uk/~nxk/USM/similar.html. The clustering of the dataset based on these similarity values can be found in http://www.cs.nott.ac.uk/~nxk/USM/clustering.html

The interface between combinatorial optimization and fuzzy sets-based methodologies is the subject of very active and increasing research. In this context we describe FANS, a fuzzy adaptive neighborhood search optimization heuristic that uses a fuzzy valuation to qualify solutions and adapts its behavior as a function of the search state. FANS may also be regarded as a local search framework. We show the application of this fuzzy sets-based heuristic to the protein structure prediction problem in two aspects: first, to analyze how the codification of the solutions affects the results, and second, to confirm that FANS is able to obtain as good results as a genetic algorithm. Both results shed some light on the application of heuristics to the protein structure prediction problem and show the benefits and power of combining basic fuzzy sets ideas with heuristic techniques. © 2002 Wiley Periodicals, Inc.

Neighborhood-based search algorithms have been applied with success in the optimization field. In this paper, the FANS (Fuzzy Adaptive Neighborhood Search) algorithm, which uses fuzzy concepts and heuristic rules in its decision procedures, together with several schedulers to adapt its behavior, is proposed. The conceptual and practical design of FANS in components allows decision makers to test different schemes and alternatives.

No abstract available.

No abstract available.