This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

An apposite and eminently readable reference for all behavioral science research and development

Encyclopedia of Statistics in Behavioral Science

A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space etc.

Selection of Views to Materialize in a Data Warehouse

A survey on new generation metaheuristic algorithms

African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

Artificial bee colony optimization for the quadratic assignment problem

High performance low cost PC hardware, and high speed LAN/WAN technologies make distributed database(DDB) systems an attractive research area. Since Dynamic programming is not feasible for optimizing queries in a DDB, we propose a GA based query optimizer and compare its performance to random and optimal algorithms. We analyzed a set of possible GA parameters and determined that two-point truncate technique using GA gives the best results. New mutation and crossover operators have also been defined and experimentally analyzed. We performed experiments on a synthetic database with replicated relations, but no horizontal or vertical fragmentation. Network links are assumed to be gigabit Ethernet. Comparisons with optimal results found by exhaustive search show that our new GA formulation performs only 20% off the optimal results and we have achieved a 50% improvement over a previous GA based algorithm.

An evolutionary genetic algorithm for optimization of distributed database queries

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor’s solutions at the initial stages of optimisation. We show experiment...

Ahmet Cosar

Papers

A survey on new generation metaheuristic algorithms

Artificial bee colony optimization for the quadratic assignment problem

An evolutionary genetic algorithm for optimization of distributed database queries

A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

Optimization of one-dimensional Bin Packing Problem with island parallel grouping genetic algorithms☆