44 research outputs found
Evolutionary Diversity Optimisation for The Traveling Thief Problem
There has been a growing interest in the evolutionary computation community
to compute a diverse set of high-quality solutions for a given optimisation
problem. This can provide the practitioners with invaluable information about
the solution space and robustness against imperfect modelling and minor
problems' changes. It also enables the decision-makers to involve their
interests and choose between various solutions. In this study, we investigate
for the first time a prominent multi-component optimisation problem, namely the
Traveling Thief Problem (TTP), in the context of evolutionary diversity
optimisation. We introduce a bi-level evolutionary algorithm to maximise the
structural diversity of the set of solutions. Moreover, we examine the
inter-dependency among the components of the problem in terms of structural
diversity and empirically determine the best method to obtain diversity. We
also conduct a comprehensive experimental investigation to examine the
introduced algorithm and compare the results to another recently introduced
framework based on the use of Quality Diversity (QD). Our experimental results
show a significant improvement of the QD approach in terms of structural
diversity for most TTP benchmark instances.Comment: To appear at GECCO 202
On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
Evolutionary algorithms have been shown to obtain good solutions for complex
optimization problems in static and dynamic environments. It is important to
understand the behaviour of evolutionary algorithms for complex optimization
problems that also involve dynamic and/or stochastic components in a systematic
way in order to further increase their applicability to real-world problems. We
investigate the node weighted traveling salesperson problem (W-TSP), which
provides an abstraction of a wide range of weighted TSP problems, in dynamic
settings. In the dynamic setting of the problem, items that have to be
collected as part of a TSP tour change over time. We first present a dynamic
setup for the dynamic W-TSP parameterized by different types of changes that
are applied to the set of items to be collected when traversing the tour. Our
first experimental investigations study the impact of such changes on resulting
optimized tours in order to provide structural insights of optimization
solutions. Afterwards, we investigate simple mutation-based evolutionary
algorithms and study the impact of the mutation operators and the use of
populations with dealing with the dynamic changes to the node weights of the
problem
Evolutionary Multi-Objective Optimization for the Dynamic Knapsack Problem
Evolutionary algorithms are bio-inspired algorithms that can easily adapt to
changing environments. In this paper, we study single- and multi-objective
baseline evolutionary algorithms for the classical knapsack problem where the
capacity of the knapsack varies over time. We establish different benchmark
scenarios where the capacity changes every iterations according to a
uniform or normal distribution. Our experimental investigations analyze the
behavior of our algorithms in terms of the magnitude of changes determined by
parameters of the chosen distribution, the frequency determined by , and
the class of knapsack instance under consideration. Our results show that the
multi-objective approaches using a population that caters for dynamic changes
have a clear advantage in many benchmarks scenarios when the frequency of
changes is not too high. Furthermore, we demonstrate that the distribution
handling techniques in advance algorithms such as NSGA-II and SPEA2 do not
necessarily result in better performance and even prevent these algorithms from
finding good quality solutions in comparison with simple multi-objective
approaches