2,456 research outputs found
Firefly Algorithm: Recent Advances and Applications
Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.Comment: 15 page
On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment
This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings
The design and applications of the african buffalo algorithm for general optimization problems
Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development
of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’
stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful
grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained,
separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the
successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature
Particle Swarm Optimization
Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field
A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles
Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
An optimised cuckoo-based discrete symbiotic organisms search strategy for tasks scheduling in cloud computing environment
Currently, the cloud computing paradigm is experiencing rapid growth as there
is a shift from other distributed computing methods and traditional IT
infrastructure towards it. Consequently, optimised task scheduling techniques
have become crucial in managing the expanding cloud computing environment. In
cloud computing, numerous tasks need to be scheduled on a limited number of
diverse virtual machines to minimise the imbalance between the local and global
search space; and optimise system utilisation. Task scheduling is a challenging
problem known as NP-complete, which means that there is no exact solution, and
we can only achieve near-optimal results, particularly when using large-scale
tasks in the context of cloud computing. This paper proposes an optimised
strategy, Cuckoo-based Discrete Symbiotic Organisms Search (C-DSOS) that
incorporated with Levy-Flight for optimal task scheduling in the cloud
computing environment to minimise degree of imbalance. The strategy is based on
the Standard Symbiotic Organism Search (SOS), which is a nature-inspired
metaheuristic optimisation algorithm designed for numerical optimisation
problems. SOS simulates the symbiotic relationships observed in ecosystems,
such as mutualism, commensalism, and parasitism. To evaluate the proposed
technique, the CloudSim toolkit simulator was used to conduct experiments. The
results demonstrated that C-DSOS outperforms the Simulated Annealing Symbiotic
Organism Search (SASOS) algorithm, which is a benchmarked algorithm commonly
used in task scheduling problems. C-DSOS exhibits a favourable convergence
rate, especially when using larger search spaces, making it suitable for task
scheduling problems in the cloud. For the analysis, a t-test was employed,
reveals that C-DSOS is statistically significant compared to the benchmarked
SASOS algorithm, particularly for scenarios involving a large search space.Comment: 21 pages, 5 figures, 2 algorithms, 6 table
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