Technical Reports

• D2.1.1: Unify mathematical takeover model for enhanced versions of evolutionary algorithms
• D2.1.2: Impact of the variation operators in the mathematical models for takeover calculation
• D2.2.1: Complexity analysis of canonical algorithms
• D2.2.2: Complexity analysis of developed algorithms
• D2.3.1: Autocorrelation measures for combinatorial optimization problems.
• D2.3.2: Relationship between the autocorrelation measures and the difficulty for trajectory-based methods.
• D2.4.1: Elementary landscape decomposition for combinatorial optimization problems.
• D2.4.2: Design of new operators for evolutionary algorithms based on landscape’s theory.
• D3.1.1: Multicore design strategies and implementations of metaheuristics.
• D3.1.2: GPU-enabled design strategies and implementations of metaheuristics.
• D3.2.1: Evaluate the frameworks for developing application with Android, Symbian, Windows Phone 7, iPhone/iPod devices and identify their suitability for implementing metaheuristics
• D.3.2.2: Propose several implementations of GAs, ACO, PSO, which run over the most appropriate technology selected above.
• D3.3.1: Updated survey of decentralized metaheuristics in the literature.
• D3.3.2: Updated survey of decentralized applications in VANETs.
• D3.3.3: Studies on decentralized metaheuristics for solving academic and VANET optimization problems.
• D3.4.1: Dynamic Optimization: algorithms and their performance.
• D3.4.2: Techniques to improve the performance in Dynamic Optimization.
• D3.5.1: Incorporating information into metaheuristics to design self-adaptive algorithms.
• D3.5.2: A new seftadaptive distributed algorithm for dynamic problems.
• D3.6.1: Key features of metaheuristics.
• D3.6.2: Definition of promising mechanism of hybridization.
• D3.7.1: Devising a list of requirements imposed by the contexts in which real-world problems appear (time constraints, competition between companies, etc.). Analyze the different design options for metaheuristics to deal with scalability (large number of variables) and uncertainty (noise, robustness, dynamic functions).
• D3.8.1: Accurate and efficient simulation of VANETs.
• D3.8.2: Simulating for fitness evaluation of in VANET optimization problems.
• D4.1.1: Evaluation methodology: metrics, indices and levels of performance.
• D4.2.1: Generated quality metrics and feature report on our software tests.
• D4.2.2: Influence of computing platform on the numerical performance.
• D4.3.1: Review of performance metrics in VANETs.
• D4.3.2: SW package for QoS of VANETs in Ns-2.
• D4.4.1: Analysis and evaluation of different metaheuristics on benchmarks of combinatorial optimization problems (MAXSAT, QAP, Knapsack).
• D4.4.2: Analysis and evaluation of different metaheuristics on benchmarks of continuous optimization problems (CEC/GECCO benchmarks).
• D4.4.3: Analysis and evaluation of different multiobjective metaheuristics on benchmarks of robust optimization problems (GTCO).
• D4.5.1: Beating the state-of-the-art in academic benchmarks
• D4.6.1: Analysis and evaluation of different metaheuristics using the RND problem generator.
• D4.6.2: Analysis and evaluation of different metaheuristics using the FAP problem generator.
• D4.6.3: Analysis and evaluation of different metaheuristics using the SPS problem generator.
• D4.7.1: Beating the state-of-the-art in generated instances.
• D5.1.1: A review of broadcast protocols for VANETs
• D5.1.2: Offline broadcast optimization using metaheuristics.
• D5.2.1: A survey of routing protocols for VANETs.
• D5.2.2: Offline routing optimization using metaheuristics.
• D5.2.3: Performance analysis of optimal routing protocols in real world tests.
• D5.3.1: Main issues of available distributed routing protocols in VANETs
• D5.3.2: Applying swarm intelligence to design online routing protocols for VANETs.
• D5.4.1: Requirements to deploy a real outdoor VANET
• D5.4.2: Deployment of a multi-hop VANET testbed.
• D5.5.1: Optimized parameter tuning in mobile IP for VANETs
• D5.5.2: Searching for errors in mobile IP.
• D5.6.1: Realistic traffic network instances: Andalucía benchmark tests
• D5.6.2: Optimal sceduling. of traffic lights with metaheuristics
• D5.7.1: Info Panel Location problem: modeling and instances.
• D5.7.2 PSO for IPN location problem.
• D5.8.1: A selection of information services for LED info panels.
• D5.8.2: LED info panel services.
• D5.9.1: Assigning routes in the vehicle routing problem: the dynamic case