690 research outputs found
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
Combinatorial optimization assumes that all parameters of the optimization
problem, e.g. the weights in the objective function is fixed. Often, these
weights are mere estimates and increasingly machine learning techniques are
used to for their estimation. Recently, Smart Predict and Optimize (SPO) has
been proposed for problems with a linear objective function over the
predictions, more specifically linear programming problems. It takes the regret
of the predictions on the linear problem into account, by repeatedly solving it
during learning. We investigate the use of SPO to solve more realistic discrete
optimization problems. The main challenge is the repeated solving of the
optimization problem. To this end, we investigate ways to relax the problem as
well as warmstarting the learning and the solving. Our results show that even
for discrete problems it often suffices to train by solving the relaxation in
the SPO loss. Furthermore, this approach outperforms, for most instances, the
state-of-the-art approach of Wilder, Dilkina, and Tambe. We experiment with
weighted knapsack problems as well as complex scheduling problems and show for
the first time that a predict-and-optimize approach can successfully be used on
large-scale combinatorial optimization problems
Smallest covering regions and highest density regions for discrete distributions
This paper examines the problem of computing a canonical smallest covering
region for an arbitrary discrete probability distribution. This optimisation
problem is similar to the classical 0-1 knapsack problem, but it involves
optimisation over a set that may be countably infinite, raising a computational
challenge that makes the problem non-trivial. To solve the problem we present
theorems giving useful conditions for an optimising region and we develop an
iterative one-at-a-time computational method to compute a canonical smallest
covering region. We show how this can be programmed in pseudo-code and we
examine the performance of our method. We compare this algorithm with other
algorithms available in statistical computation packages to compute HDRs. We
find that our method is the only one that accurately computes HDRs for
arbitrary discrete distributions
Robust temporal optimisation for a crop planning problem under climate change uncertainty
Considering a temporal dimension allows for the delivery of rolling solutions to complex real-world problems. Moving forward in time brings uncertainty, and large margins for potential error in solutions. For the multi-year crop planning problem, the largest uncertainty is how the climate will change over coming decades. The innovation this paper presents are novel methods that allow the solver to produce feasible solutions under all climate models tested, simultaneously. Three new measures of robustness are introduced and evaluated. The highly robust solutions are shown to vary little across different climate change projections, maintaining consistent net revenue and environmental flow deficits
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HEDCOS: High Efficiency Dynamic Combinatorial Optimization System using Ant Colony Optimization algorithm
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDynamic combinatorial optimization is gaining popularity among industrial practitioners due to the ever-increasing scale of their optimization problems and efforts to solve them to remain competitive. Larger optimization problems are not only more computationally intense to optimize but also have more uncertainty within problem inputs. If some aspects of the problem are subject to dynamic change, it becomes a Dynamic Optimization Problem (DOP).
In this thesis, a High Efficiency Dynamic Combinatorial Optimization System is built to solve challenging DOPs with high-quality solutions. The system is created using Ant Colony Optimization (ACO) baseline algorithm with three novel developments.
First, introduced an extension method for ACO algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality by solving challenging optimization problems with a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack Problem (MKP).
Second, a non-stochastic dataset generation method was introduced to solve the dynamic optimization research replicability problem. This method uses a static benchmark dataset as a starting point and source of entropy to generate a sequence of dynamic states. Then using this method, 1405 Dynamic Multidimensional Knapsack Problem (DMKP) benchmark datasets were generated and published using famous static MKP benchmark instances as the initial state.
Third, introduced a nature-inspired discrete dynamic optimization strategy for ACO by modelling real-world ants’ symbiotic relationship with aphids. ACO with Aphids strategy is designed to solve discrete domain DOPs with event-triggered discrete dynamism. The strategy improved inter-state convergence by allowing better solution recovery after dynamic environment changes. Aphids mediate the information from previous dynamic optimization states to maximize initial results performance and minimize the impact on convergence speed. This strategy is tested for DMKP and against identical ACO implementations using Full-Restart and Pheromone-Sharing strategies, with all other variables isolated.
Overall, Dynamic Impact and ACO with Aphids developments are compounding. Using Dynamic Impact on single objective optimization of MMPPFO, the fitness value was improved by 33.2% over the ACO algorithm without Dynamic Impact. MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed, and large complexity instances have shown the average gap improved by 4.26 times. ACO with Aphids has also demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2% for a total compounded dynamic optimization performance improvement of 6.02 times. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5% for a total compounded dynamic optimization performance improvement of 8.99 times
A comparative study of evolutionary approaches to the bi-objective dynamic Travelling Thief Problem
Dynamic evolutionary multi-objective optimization is a thriving research area. Recent contributions span the development of specialized algorithms and the construction of challenging benchmark problems. Here, we continue these research directions through the development and analysis of a new bi-objective problem, the dynamic Travelling Thief Problem (TTP), including three modes of dynamic change: city locations, item profit values, and item availability. The interconnected problem components embedded in the dynamic problem dictate that the effective tracking of good trade-off solutions that satisfy both objectives throughout dynamic events is non-trivial. Consequently, we examine the relative contribution to the non-dominated set from a variety of population seeding strategies, including exact solvers and greedy algorithms for the knapsack and tour components, and random techniques. We introduce this responsive seeding extension within an evolutionary algorithm framework. The efficacy of alternative seeding mechanisms is evaluated across a range of exemplary problem instances using ranking-based and quantitative statistical comparisons, which combines performance measurements taken throughout the optimization. Our detailed experiments show that the different dynamic TTP instances present varying difficulty to the seeding methods tested. We posit the dynamic TTP as a suitable benchmark capable of generating problem instances with different controllable characteristics aligning with many real-world problems
Genetic algorithms with implicit memory
This thesis investigates the workings of genetic algorithms in
dynamic optimisation problems where fitness landscapes materialise
that are identical to, or resemble in some way, landscapes
previously encountered. The objective is to appraise the
performances of the various approaches offered by the GAs.
Approaches specifically tailored for different kinds of dynamic
environment lie outside the remit of the thesis.
The main topics that are explored are: genetic redundancy,
modularity, neutral evolution, explicit memory, and implicit memory.
It is in the matter of implicit memory that the thesis makes the
majority of its novel contributions. It is demonstrated via
experimental analysis that the pre-existing techniques are
deficient, and a new algorithm – the pointer genetic algorithm
(pGA) – is expounded and assessed in an attempt to offer an
improvement. It is shown that though it outperforms its rivals, it
cannot attain the performance levels of an explicit memory algorithm
(that is, an algorithm using an external memory bank).
The main claims of the thesis are that with regard to memory, the
pre-existing implicit-memory algorithms are deficient, the new
pointer GA is superior, and that because all of the implicit
approaches are inferior to explicit approaches, it is explicit
approaches that should be used in real-world problem solving
Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
Consider the setting of constrained optimization, with some parameters
unknown at solving time and requiring prediction from relevant features.
Predict+Optimize is a recent framework for end-to-end training supervised
learning models for such predictions, incorporating information about the
optimization problem in the training process in order to yield better
predictions in terms of the quality of the predicted solution under the true
parameters. Almost all prior works have focused on the special case where the
unknowns appear only in the optimization objective and not the constraints. Hu
et al.~proposed the first adaptation of Predict+Optimize to handle unknowns
appearing in constraints, but the framework has somewhat ad-hoc elements, and
they provided a training algorithm only for covering and packing linear
programs. In this work, we give a new \emph{simpler} and \emph{more powerful}
framework called \emph{Two-Stage Predict+Optimize}, which we believe should be
the canonical framework for the Predict+Optimize setting. We also give a
training algorithm usable for all mixed integer linear programs, vastly
generalizing the applicability of the framework. Experimental results
demonstrate the superior prediction performance of our training framework over
all classical and state-of-the-art methods
Comparison of optimisation algorithms for centralised anaerobic co-digestion in a real river basin case study in Catalonia
Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.Peer ReviewedPostprint (published version
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