4,943 research outputs found
Hybrid nature-inspired computation methods for optimization
The focus of this work is on the exploration of the hybrid Nature-Inspired Computation (NIC) methods with application in optimization. In the dissertation, we first study various types of the NIC algorithms including the Clonal Selection Algorithm (CSA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), Harmony Search (HS), Differential Evolution (DE), and Mind Evolution Computing (MEC), and propose several new fusions of the NIC techniques, such as CSA-DE, HS-DE, and CSA-SA. Their working principles, structures, and algorithms are analyzed and discussed in details. We next investigate the performances of our hybrid NIC methods in handling nonlinear, multi-modal, and dynamical optimization problems, e.g., nonlinear function optimization, optimal LC passive power filter design, and optimization of neural networks and fuzzy classification systems. The hybridization of these NIC methods can overcome the shortcomings of standalone algorithms while still retaining all the advantages. It has been demonstrated using computer simulations that the proposed hybrid NIC approaches are capable of yielding superior optimization performances over the individual NIC methods as well as conventional methodologies with regard to the search efficiency, convergence speed, and quantity and quality of the optimal solutions achieved
Chaotic Sand Cat Swarm Optimization
In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm
combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of
the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core
search process to improve global search performance and convergence behavior. Thus, randomness
in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical
and dynamic properties. In addition to these advantages, low search consistency, local optimum trap,
inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO,
several chaotic maps are implemented for more efficient behavior in the exploration and exploitation
phases. Experiments are conducted on a wide variety of well-known test functions to increase the
reliability of the results, as well as real-world problems. In this study, the proposed algorithm was
applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses
compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This
extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results
Leo: Lagrange Elementary Optimization
Global optimization problems are frequently solved using the practical and
efficient method of evolutionary sophistication. But as the original problem
becomes more complex, so does its efficacy and expandability. Thus, the purpose
of this research is to introduce the Lagrange Elementary Optimization (Leo) as
an evolutionary method, which is self-adaptive inspired by the remarkable
accuracy of vaccinations using the albumin quotient of human blood. They
develop intelligent agents using their fitness function value after gene
crossing. These genes direct the search agents during both exploration and
exploitation. The main objective of the Leo algorithm is presented in this
paper along with the inspiration and motivation for the concept. To demonstrate
its precision, the proposed algorithm is validated against a variety of test
functions, including 19 traditional benchmark functions and the CECC06 2019
test functions. The results of Leo for 19 classic benchmark test functions are
evaluated against DA, PSO, and GA separately, and then two other recent
algorithms such as FDO and LPB are also included in the evaluation. In
addition, the Leo is tested by ten functions on CECC06 2019 with DA, WOA, SSA,
FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate
Leo's capacity to increase the starting population and move toward the global
optimum. Different standard measurements are used to verify and prove the
stability of Leo in both the exploration and exploitation phases. Moreover,
Statistical analysis supports the findings results of the proposed research.
Finally, novel applications in the real world are introduced to demonstrate the
practicality of Leo.Comment: 28 page
Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments
Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments
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