2,687 research outputs found
Orthogonal methods based ant colony search for solving continuous optimization problems
Research into ant colony algorithms for solving continuous optimization problems forms one of the most
significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial
optimization, they have shown great potential in solving a wide range of optimization problems, including continuous
optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for
solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore
their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed
algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is
compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions
in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Cooperative Particle Swarm Optimization for Combinatorial Problems
A particularly successful line of research for numerical optimization is the well-known computational paradigm particle swarm optimization (PSO). In the PSO framework, candidate solutions are represented as particles that have a position and a velocity in a multidimensional search space. The direct representation of a candidate solution as a point that flies through hyperspace (i.e., Rn) seems to strongly predispose the PSO toward continuous optimization. However, while some attempts have been made towards developing PSO algorithms for combinatorial problems, these techniques usually encode candidate solutions as permutations instead of points in search space and rely on additional local search algorithms.
In this dissertation, I present extensions to PSO that by, incorporating a cooperative strategy, allow the PSO to solve combinatorial problems. The central hypothesis is that by allowing a set of particles, rather than one single particle, to represent a candidate solution, combinatorial problems can be solved by collectively constructing solutions. The cooperative strategy partitions the problem into components where each component is optimized by an individual particle. Particles move in continuous space and communicate through a feedback mechanism. This feedback mechanism guides them in the assessment of their individual contribution to the overall solution.
Three new PSO-based algorithms are proposed. Shared-space CCPSO and multispace CCPSO provide two new cooperative strategies to split the combinatorial problem, and both models are tested on proven NP-hard problems. Multimodal CCPSO extends these combinatorial PSO algorithms to efficiently sample the search space in problems with multiple global optima. Shared-space CCPSO was evaluated on an abductive problem-solving task: the construction of parsimonious set of independent hypothesis in diagnostic problems with direct causal links between disorders and manifestations. Multi-space CCPSO was used to solve a protein structure prediction subproblem, sidechain packing. Both models are evaluated against the provable optimal solutions and results show that both proposed PSO algorithms are able to find optimal or near-optimal solutions. The exploratory ability of multimodal CCPSO is assessed by evaluating both the quality and diversity of the solutions obtained in a protein sequence design problem, a highly multimodal problem. These results provide evidence that extended PSO algorithms are capable of dealing with combinatorial problems without having to hybridize the PSO with other local search techniques or sacrifice the concept of particles moving throughout a continuous search space
Efficiency Analysis of Swarm Intelligence and Randomization Techniques
Swarm intelligence has becoming a powerful technique in solving design and
scheduling tasks. Metaheuristic algorithms are an integrated part of this
paradigm, and particle swarm optimization is often viewed as an important
landmark. The outstanding performance and efficiency of swarm-based algorithms
inspired many new developments, though mathematical understanding of
metaheuristics remains partly a mystery. In contrast to the classic
deterministic algorithms, metaheuristics such as PSO always use some form of
randomness, and such randomization now employs various techniques. This paper
intends to review and analyze some of the convergence and efficiency associated
with metaheuristics such as firefly algorithm, random walks, and L\'evy
flights. We will discuss how these techniques are used and their implications
for further research.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:1212.0220, arXiv:1208.0527, arXiv:1003.146
A new Taxonomy of Continuous Global Optimization Algorithms
Surrogate-based optimization, nature-inspired metaheuristics, and hybrid
combinations have become state of the art in algorithm design for solving
real-world optimization problems. Still, it is difficult for practitioners to
get an overview that explains their advantages in comparison to a large number
of available methods in the scope of optimization. Available taxonomies lack
the embedding of current approaches in the larger context of this broad field.
This article presents a taxonomy of the field, which explores and matches
algorithm strategies by extracting similarities and differences in their search
strategies. A particular focus lies on algorithms using surrogates,
nature-inspired designs, and those created by design optimization. The
extracted features of components or operators allow us to create a set of
classification indicators to distinguish between a small number of classes. The
features allow a deeper understanding of components of the search strategies
and further indicate the close connections between the different algorithm
designs. We present intuitive analogies to explain the basic principles of the
search algorithms, particularly useful for novices in this research field.
Furthermore, this taxonomy allows recommendations for the applicability of the
corresponding algorithms.Comment: 35 pages total, 28 written pages, 4 figures, 2019 Reworked Versio
A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades:Part B
Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out
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