87 research outputs found
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
An efficient evolutionary algorithm for the orienteering problem
This paper deals with the Orienteering Problem, which is a routing problem. In the Orienteering Problem, each node has a profit assigned and the goal is to find the route that maximizes the total collected profit subject to a limitation on the total route distance. To solve this problem, we propose an evolutionary algorithm, whose key characteristic is to maintain unfeasible solutions during the search. Furthermore, it includes a novel solution codification for the Orienteering Problem, a novel heuristic for node inclusion in the route, an adaptation of the Edge Recombination crossover developed for the Travelling Salesperson Problem, specific operators to recover the feasibility of solutions when required, and the use of the Lin-Kernighan heuristic to improve the route lengths. We compare our algorithm with three state-of-the-art algorithms for the problem on 344 benchmark
instances, with up to 7397 nodes. The results show a competitive behavior of our approach in instances of low-medium dimensionality, and outstanding results in the large dimensionality instances reaching new best known solutions with lower computational time than the state-of-the-art algorithms.MTM2015-65317-P, TIN2016-78365-R, IT-609-13, IT-928-16, UFI BETS 201
AN EFFECTIVE METAHEURISTIC FOR TOURIST TRIP PLANNING IN PUBLIC TRANSPORT NETWORKS
The Time-Dependent Orienteering Problem with Time Windows (TDOPTW) is a combinatorial optimization problem defined on graphs. Its real life applications are particularly associated with tourist trip planning in trans-port networks, where travel time between two points depends on the moment of travel start. In the paper an effective TDOPTW solution (evolutionary algorithm with local search operators) was presented and applied to gen-erate attractive tours in real public transport networks of Białystok and Athens. The method achieved very high-quality solutions in a short execution time
20 years of Greedy Randomized Adaptive Search Procedures with Path Relinking
This is a comprehensive review of the Greedy Randomized Adaptive Search
Procedure (GRASP) metaheuristic and its hybridization with Path Relinking (PR)
over the past two decades. GRASP with PR has become a widely adopted approach
for solving hard optimization problems since its proposal in 1999. The paper
covers the historical development of GRASP with PR and its theoretical
foundations, as well as recent advances in its implementation and application.
The review includes a critical analysis of variants of PR, including
memory-based and randomized designs, with a total of ten different
implementations. It describes these advanced designs both theoretically and
practically on two well-known optimization problems, linear ordering and
max-cut. The paper also explores the hybridization of GRASP with PR and other
metaheuristics, such as Tabu Search and Scatter Search. Overall, this review
provides valuable insights for researchers and practitioners seeking to utilize
GRASP with PR for solving optimization problems.Comment: 28 pages, 13 figure
Comparação de dois algoritmos genéticos aplicados ao TOP
A recolha seletiva de resÃduos sólidos urbanos para reciclagem é um processo dispendioso, especialmente quando realizado em grande escala. Um problema importante neste processo reside na gestão de uma frota, uma vez que atualmente as estratégias utilizadas geralmente têm baixa eficiência.
O processo de recolha seletiva de resÃduos sólidos urbanos pode ser modelado como um problema de encaminhamento de veÃculos, em particular como um Problema de Orientação de Equipas (TOP - Team Orienteering Problem). No TOP uma frota de veÃculos é incumbida de visitar um conjunto selecionado de vértices, de modo a maximizar o luvro total. O objetivo deste trabalho é o de otimizar o processo de recolha selectiva de resÃduos sólidos urbanos ao abordar as questões relacionadas com a gestão de uma frota. Isso deve ser alcançado através do desenvolvimento de uma ferramenta de software que implementa um algoritmo genético para resolver o modelo desenvolvido.
Neste artigo apresentamos e comparamos dois algoritmos genéticos através de experiências computacionais realizadas com instâncias de teste conhecidas da literatura. O uso de algoritmos genéticos para resolver o TOP mostra ser uma escolha acertada, pois o método é eficiente produzindo bons resultados num tempo aceitável.Fundos FEDER através do Programa Operacional Fatores de Competitividade – COMPETE e por Fundos Nacionais através da FCT – Fundação para a Ciência e Tecnologia no âmbito do Projeto: FCOMP-01-0124-FEDER-022674GATOP - Genetic Algorithms for Team Orienteering Problem (Ref PTDC/EME-GIN/ 120761/2010), financiado por fundos nacionais pela FCT / MCTES e co-financiado pelo by the European Social Development Fund (FEDER) through the COMPETE Programa Operacional Fatores de Competitividade (POFC) Ref FCOMP-01-0124-FEDER-020609
The Vehicle Routing Problem with Service Level Constraints
We consider a vehicle routing problem which seeks to minimize cost subject to
service level constraints on several groups of deliveries. This problem
captures some essential challenges faced by a logistics provider which operates
transportation services for a limited number of partners and should respect
contractual obligations on service levels. The problem also generalizes several
important classes of vehicle routing problems with profits. To solve it, we
propose a compact mathematical formulation, a branch-and-price algorithm, and a
hybrid genetic algorithm with population management, which relies on
problem-tailored solution representation, crossover and local search operators,
as well as an adaptive penalization mechanism establishing a good balance
between service levels and costs. Our computational experiments show that the
proposed heuristic returns very high-quality solutions for this difficult
problem, matches all optimal solutions found for small and medium-scale
benchmark instances, and improves upon existing algorithms for two important
special cases: the vehicle routing problem with private fleet and common
carrier, and the capacitated profitable tour problem. The branch-and-price
algorithm also produces new optimal solutions for all three problems
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