223 research outputs found
Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues
Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm
inspired by the convergence mechanism to the center of potential generated
within a single well of a spatially co-located double-delta well setup. It
mimics the wave nature of candidate positions in solution spaces and draws upon
quantum mechanical interpretations much like other quantum-inspired
computational intelligence paradigms. In this work, we introduce a Chebyshev
map driven chaotic perturbation in the optimization phase of the algorithm to
diversify weights placed on contemporary and historical, socially-optimal
agents' solutions. We follow this up with a characterization of solution
quality on a suite of 23 single-objective functions and carry out a comparative
analysis with eight other related nature-inspired approaches. By comparing
solution quality and successful runs over dynamic solution ranges, insights
about the nature of convergence are obtained. A two-tailed t-test establishes
the statistical significance of the solution data whereas Cohen's d and Hedge's
g values provide a measure of effect sizes. We trace the trajectory of the
fittest pseudo-agent over all function evaluations to comment on the dynamics
of the system and prove that the proposed algorithm is theoretically globally
convergent under the assumptions adopted for proofs of other closely-related
random search algorithms.Comment: 27 pages, 4 figures, 19 table
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.Comment: 35 page
A quantum behaved particle swarm approach to multi-objective optimization
Many real-world optimization problems have multiple objectives that have to be optimized simultaneously. Although a great deal of effort has been devoted to solve multi-objective optimization problems, the problem is still open and the related issues still attract significant research efforts. Quantum-behaved Particle Swarm Optimization (QPSO) is a recently proposed population based metaheuristic that relies on quantum mechanics principles. Since its inception, much effort has been devoted to develop improved versions of QPSO designed for single objective optimization. However, many of its advantages are not yet available for multi-objective optimization. In this thesis, we develop a new framework for multi-objective problems using QPSO. The contribution of the work is threefold. First a hybrid leader selection method has been developed to compute the attractor of a given particle. Second, an archiving strategy has been proposed to control the growth of the archive size. Third, the developed framework has been further extended to handle constrained optimization problems. A comprehensive investigation of the developed framework has been carried out under different selection, archiving and constraint handling strategies. The developed framework is found to be a competitive technique to tackle this type of problems when compared against the state-of-the-art methods in multi-objective optimization
A Simple Brain Storm Optimization Algorithm with a Periodic Quantum Learning Strategy
Brain storm optimization (BSO) is a young and promising population-based swarm intelligence algorithm inspired by the human process of brainstorming. The BSO algorithm has been successfully applied to both science and engineering issues. However, thus far, most BSO algorithms are prone to fall into local optima when solving complicated optimization problems. In addition, these algorithms adopt complicated clustering strategies such as K-means clustering, resulting in large computational burdens. The paper proposes a simple BSO algorithm with a periodic quantum learning strategy (SBSO-PQLS), which includes three new strategies developed to improve the defects described above. First, we develop a simple individual clustering (SIC) strategy that sorts individuals according to their fitness values and then allocates all individuals into different clusters. This reduces computational burdens and resists premature convergence. Second, we present a simple individual updating (SIU) strategy by simplifying the individual combinations and improving the step size function to enrich the diversity of newly generated individuals and reduces redundancy in the pattern for generating individuals. Third, a quantum-behaved individual updating with periodic learning (QBIU-PL) strategy is developed by introducing a quantum-behaved mechanism into SBSO-PQLS. QBIU-PL provides new momentum, enabling individuals to escape local optima. With the support of these three strategies, SBSO-PQLS effectively improves its global search capability and computational burdens. SBSO-PQLS is compared with seven other BSO variants, Particle Swarm Optimization (PSO), and Differential Evolution (DE) on CEC2013 benchmark functions. The results show that SBSO-PQLS achieves a better global search performance than do the other nine algorithms
A comprehensive survey on cultural algorithms
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