Juan Ramón González

Optimization in dynamic environments: a survey on problems, methods and measures

This paper provides a survey of the research done on optimization in dynamic environments over the past decade. We show an analysis of the most commonly used problems, methods and measures together with the newer approaches and trends, as well as their interrelations and common ideas. The survey is supported by a public web repository, located at http://www.dynamic-optimization.org where the collected bibliography is manually organized and tagged according to different categories.

A simple and fast heuristic for protein structure comparison

BackgroundProtein 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.ResultsWe 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 SCOPs family and CATHs architecture levels using normalized overlap values. Some limitations and the role of normalization are outlined for doing classification at SCOPs fold level.ConclusionWe designed, implemented and tested.a new tool for solving MAX-CMO, based on a well-known metaheuristic technique. The good balance between solutions 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 SCOPs fold and CATHs architecture levels with encouraging results.Software is available for download at http://modo.ugr.es/jrgonzalez/msvns4maxcmo.

Using heuristic rules to enhance a multiswarm PSO for dynamic environments

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.

An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems

The particle swarm optimization (PSO) algorithm has successfully been applied to dynamic optimization problems with very competitive results. One of its best performing variants is the one based on the atomic model, with quantum and trajectory particles. However, there is no precise knowledge on how these particles contribute to the global behavior of the swarms during the optimization process. This work analyzes several aspects of each type of particle, including the best combination of them for different scenarios, and how many times do they contribute to the swarms best. Results show that, for the Moving Peaks Benchmark (MPB), a higher number of trajectory particles than quantum particles is the best strategy. Quantum particles are most helpful immediately after a change in the environment has occurred, while trajectory particles lead the optimization in the final stages. Suggestions on how to use this knowledge for future developments are also provided.

An Adaptive Multiagent Strategy for Solving Combinatorial Dynamic Optimization Problems

This work presents the results obtained when using a decentralised multiagent strategy (Agents) to solve dynamic optimization problems of a combinatorial nature. To improve the results of the strategy, we also include a simple adaptive scheme for several configuration variants of a mutation operator in order to obtain a more robust behaviour. The adaptive scheme is also tested on an evolutionary algorithm (EA). Finally, both Agents and EA are compared against the recent state of the art adaptive hill-climbing memetic algorithm (AHMA).

Cooperation rules in a trajectory-based centralised cooperative strategy for Dynamic Optimisation Problems

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.

On Using Fuzzy Contact Maps for Protein Structure Comparison

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 maps 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.

Guest Editorial: Special Issue on Nature Inspired Cooperative Strategies for Optimization (Part II)

Nature is an unlimited source of resilient, robust, fine tuned,complex mechanisms and phenomena which have been thesubject of exploration and exploitation in science and engi-neering. In particular, understanding complex cooperativesystems in nature has driven computer scientists and prac-titionerstooutstandingdevelopmentsrangingfromversatilemodels and architectures to highly effective methodologiesand computing strategies. Examples of these include, butare not limited to, artificial bee colony, memetic-computingagent-models, bacteria metabolism inspired robot control-lers, evolutionary design optimization, particle swarm opti-mization, emergent collective robotics behavior to name butafew.This thematic issue brings a selection of the best researchworkspublishedintheIVInternationalWorkshoponNatureInspired Cooperative Strategies for Optimization (NICSO)held on May 2010 in Granada, Spain. The aim of NICSO istobringtogetherscientistsfromallovertheworldtodiscussthe latest ideas and state of the art on nature inspired coop-erative strategies. As in its previous editions, NICSO 2010hascoveredtopicsrelatedtoadaptivebehavior,antcolonies,amorphous computing, artificial life, bio-inspired architec-tures, distributed computing, evolutionary robotics, evolv-ablesystems,membranecomputing,softwareself-assembly,evolutionary computation, swarm intelligence and quantum

Solving Bioinformatics Problems by Soft Computing Techniques: Protein Structure Comparison as Example

Bioinformatics is a very interesting an active area that tackles difficult problems with lots of data that may have noise, missing values, uncertainties, etc. This chapter shows how the techniques that Soft Computing provides are appropriate to solve some Bioinformatics problems. This idea is then illustrated by showing several resolution techniques for one of the key problems of the Bioinformatics area: the Protein Structure Comparison problem.