45 research outputs found
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm
The artificial fish swarm algorithm has recently been emerged in continuous global
optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.The authors wish to thank three anonymous referees for their comments and valuable suggestions to improve the paper. The first author acknowledges Ciˆencia 2007 of FCT (Foundation for Science and Technology) Portugal for the fellowship grant C2007-UMINHO-ALGORITMI-04. Financial support from FEDER COMPETE (Operational Programme Thematic Factors of Competitiveness) and FCT under project FCOMP-01-0124-FEDER-022674 is also acknowledged
Applied (Meta)-Heuristic in Intelligent Systems
Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems
Sine Cosine Algorithm for Optimization
This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
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
Traveling Salesman Problem
The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance
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Enhancing the Performance of Search Heuristics. Variable Fitness Functions and other Methods to Enhance Heuristics for Dynamic Workforce Scheduling.
Scheduling large real world problems is a complex process and finding high quality
solutions is not a trivial task. In cooperation with Trimble MRM Ltd., who provide
scheduling solutions for many large companies, a problem is identified and modelled. It
is a general model which encapsulates several important scheduling, routing and
resource allocation problems in literature. Many of the state-of-the-art heuristics for
solve scheduling problems and indeed other problems require specialised heuristics
tailored for the problem they are to solve. While these provide good solutions a lot of
expert time is needed to study the problem, and implement solutions.
This research investigates methods to enhance existing search based methods.
We study hyperheuristic techniques as a general search based heuristic. Hyperheuristics
raise the generality of the solution method by using a set of tools (low level heuristics)
to work on the solution. These tools are problem specific and usually make small
changes to the problem. It is the task of the hyperheuristic to determine which tool to
use and when. Low level heuristics using exact/heuristic hybrid method are used in this
thesis along with a new Tabu based hyperheuristic which decreases the amount of CPU
time required to produce good quality solutions. We also develop and investigate the
Variable Fitness Function approach, which provides a new way of enhancing most
search-based heuristics in terms of solution quality. If a fitness function is pushing hard
in a certain direction, a heuristic may ultimately fail because it cannot escape local
minima. The Variable Fitness Function allows the fitness function to change over the
search and use objective measures not used in the fitness calculation. The Variable
Fitness Function and its ability to generalise are extensively tested in this thesis.
The two aims of the thesis are achieved and the methods are analysed in depth.
General conclusions and areas of future work are also identified
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations
In recent years, a great variety of nature- and bio-inspired algorithms has
been reported in the literature. This algorithmic family simulates different
biological processes observed in Nature in order to efficiently address complex
optimization problems. In the last years the number of bio-inspired
optimization approaches in literature has grown considerably, reaching
unprecedented levels that dark the future prospects of this field of research.
This paper addresses this problem by proposing two comprehensive,
principle-based taxonomies that allow researchers to organize existing and
future algorithmic developments into well-defined categories, considering two
different criteria: the source of inspiration and the behavior of each
algorithm. Using these taxonomies we review more than three hundred
publications dealing with nature-inspired and bio-inspired algorithms, and
proposals falling within each of these categories are examined, leading to a
critical summary of design trends and similarities between them, and the
identification of the most similar classical algorithm for each reviewed paper.
From our analysis we conclude that a poor relationship is often found between
the natural inspiration of an algorithm and its behavior. Furthermore,
similarities in terms of behavior between different algorithms are greater than
what is claimed in their public disclosure: specifically, we show that more
than one-third of the reviewed bio-inspired solvers are versions of classical
algorithms. Grounded on the conclusions of our critical analysis, we give
several recommendations and points of improvement for better methodological
practices in this active and growing research field.Comment: 76 pages, 6 figure