11 research outputs found
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
Metaheuristics for university course timetabling.
The work presented in this thesis concerns the problem of timetabling at universities â particularly course-timetabling, and examines the various ways in which metaheuristic techniques might be applied to these sorts of problems. Using a popular benchmark version of a university course timetabling problem, we examine the implications of using a âtwostagedâ algorithmic approach, whereby in stage-one only the mandatory constraints areconsidered for satisfaction, with stage-two then being concerned with satisfying the remaining constraints but without re-breaking any of the mandatory constraints in the process. Consequently, algorithms for each stage of this approach are proposed and analysed in detail.For the first stage we examine the applicability of the so-called Grouping Genetic Algorithm (GGA). In our analysis of this algorithm we discover a number of scaling-upissues surrounding the general GGA approach and discuss various reasons as to why this is so. Two separate ways of enhancing general performance are also explored. Secondly, an Iterated Heuristic Search algorithm is also proposed for the same problem, and in experiments it is shown to outperform the GGA in almost all cases. Similar observations to these are also witnessed in a second set of experiments, where the analogous problem of colouring equipartite graphs is also considered.Two new metaheuristic algorithms are also proposed for the second stage of the twostaged approach: an evolutionary algorithm (with a number of new specialised evolutionaryoperators), and a simulated annealing-based approach. Detailed analyses of both algorithms are presented and reasons for their relative benefits and drawbacks are discussed.Finally, suggestions are also made as to how our best performing algorithms might be modified in order to deal with further âreal-worldâ constraints. In our analyses of these modified algorithms, as well as witnessing promising behaviour in some cases, we are also able to highlight some of the limitations of the two-stage approach in certain cases
Antisocial behavior identification from Twitter feeds using traditional machine learning algorithms and deep learning
Antisocial behavior (ASB) is one of the ten personality disorders included in âThe Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and falls in the same cluster as Borderline Personality Disorder, Histrionic Personality Disorder, and Narcissistic Personality Disorder. It is a prevalent pattern of disregard for and violation of the rights of others. Online antisocial behavior is a social problem and a public health threat. An act of ASB might be fun for a perpetrator; however, it can drive a victim into depression, self-confinement, low self-esteem, anxiety, anger, and suicidal ideation. Online platforms such as Twitter and Reddit can sometimes become breeding grounds for such behavior by allowing people suffering from ASB disorder to manifest their behavior online freely. In this paper, we propose a proactive approach based on natural language processing and deep learning that can enable online platforms to actively look for the signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can prevent dire situations leading to someone committing suicide
Identifying sources of global contention in constraint satisfaction search
Much work has been done on learning from failure in search to boost solving of
combinatorial problems, such as clause-learning and clause-weighting in boolean
satisfiability (SAT), nogood and explanation-based learning, and constraint weighting
in constraint satisfaction problems (CSPs). Many of the top solvers in SAT use
clause learning to good effect. A similar approach (nogood learning) has not had
as large an impact in CSPs. Constraint weighting is a less fine-grained approach
where the information learnt gives an approximation as to which variables may be
the sources of greatest contention.
In this work we present two methods for learning from search using restarts,
in order to identify these critical variables prior to solving. Both methods are
based on the conflict-directed heuristic (weighted-degree heuristic) introduced by
Boussemart et al. and are aimed at producing a better-informed version of the
heuristic by gathering information through restarting and probing of the search
space prior to solving, while minimizing the overhead of these restarts.
We further examine the impact of different sampling strategies and different
measurements of contention, and assess different restarting strategies for the
heuristic. Finally, two applications for constraint weighting are considered in
detail: dynamic constraint satisfaction problems and unary resource scheduling
problems
Exact and evolutionary algorithms for the score-constrained packing problem
This thesis concerns the Score-Constrained Packing Problem (SCPP), a combinatorial
optimisation problem related to the one-dimensional bin packing problem. The
aim of the SCPP is to pack a set of rectangular items from left to right into the
fewest number of bins such that no bin is overfilled; however, the order and orientation
of the items in each bin affects the feasibility of the overall solution. The SCPP
has applications in the packaging industry, and obtaining high quality solutions for
instances of the SCPP has the ability to reduce the amount of waste material, costs,
and time, which motivates the study in this thesis.
The minimal existing research on the SCPP leads us to explore a wide range of
approaches to the problem in this thesis, implementing ideas from related problems
in literature as well as bespoke methods. To begin, we present an exact algorithm
that can produce a feasible configuration of a subset of items in a single bin in
polynomial-time. We then introduce a range of methods for the SCPP including
heuristics, an evolutionary algorithm framework comprising a local search procedure
and a choice of three distinct recombination operators, and two algorithms combining
metaheuristics with an exact procedure. Each method is investigated to gain more
insight into the characteristics that benefit or hinder the improvement of solutions,
both theoretically and computationally, using a large number of problem instances
with varying parameters. This allows us to determine the specific methods and
properties that produce superior solutions depending on the type of problem instance
Parallelised and vectorised ant colony optimization
Ant Colony Optimisation (ACO) is a versatile population-based optimisation metaheuristic
based on the foraging behaviour of certain species of ant, and is part of the
Evolutionary Computation family of algorithms. While ACO generally provides good
quality solutions to the problems it is applied to, two key limitations prevent it from
being truly viable on large-scale problems: A high memory requirement that grows
quadratically with instance size, and high execution time. This thesis presents a parallelised
and vectorised implementation of ACO using OpenMP and AVX SIMD instructions;
while this alone is enough to improve upon the execution time of the algorithm,
this implementation also features an alternative memory structure and a novel candidate
set approach, the use of which significantly reduces the memory requirement of
ACO. This parallelism is enabled through the use of Max-Min Ant System, an ACO
variant that only utilises local memory during the solution process and therefore risks
no synchronisation issues, and an adaptation of vRoulette, a vector-compatible variant
of the common roulette wheel selection method. Through the use of these techniques
ACO is also able to find good quality solutions for the very large Art TSPs, a problem
set that has traditionally been unfeasible to solve with ACO due to high memory
requirements and execution time. These techniques can also benefit ACO when it
comes to solving other problems. In this case the Virtual Machine Placement problem,
in which Virtual Machines have to be efficiently allocated to Physical Machines in a
cloud environment, is used as a benchmark, with significant improvements to execution
time