In this paper, an unbiased comparative assessment scheme for algorithmic performances of five novel nature-inspired metaheuristic algorithms in design optimization of steel frames made out of cold-formed steel section...
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In this paper, an unbiased comparative assessment scheme for algorithmic performances of five novel nature-inspired metaheuristic algorithms in design optimization of steel frames made out of cold-formed steel sections under consideration of seismic loading is presented. These contemporary algorithms are so-called tree seed, squirrel search, water strider, grey wolf, and brain storm optimization. The functionality of the proposed algorithms is appraised with respect to design precisions in both portal and space cold-formed steel frames formulated according to the design provisions implemented by AISI-LRFD (American Iron and Steel Institute-Load and Resistance Factor Design). The cross-sectional dimensions of steel profiles, which are selected from available set of cold-formed thin-walled single-C sections, are treated as design variables in the optimization process in order to minimize the structural weight of the frames. In addition to specification constraint requirements, lateral and vertical displacement restrictions of the structural elements required for stability of the frames are also taken into account. Design optimization algorithms necessitate the structural response of cold-formed steel frames under load combinations including seismic loading effects which is accomplished by utilizing the open application programming interface (OAPI) mastery of MATLAB with SAP2000. The design optimization of cold-formed steel frames that is a discrete nonlinear programming problem reveal the robustness and applicability of proposed contemporary nature-inspired metaheuristic algorithms in real-sized complex structural optimization problems.
Software-defined Networking (SDN) offers flexibility and programmability, making it a desirable option for modern network architecture. SDN provides numerous benefits to network administrators due to its centralized c...
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Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters...
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Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters of the kernel function and the number of components of the KPLS regression to maximize its predictive ability. A metaheuristic optimization problem was proposed taking the cumulative cross-validation coefficient as an objective function to be maximized. It was solved using nature-inspired metaheuristic algorithms: the genetic algorithm, particle swarm optimization, grey wolf optimization and the firefly algorithm. To validate the results and have a reference measure of the efficiency of the nature-inspired metaheuristic algorithms, derivative-free optimization algorithms were also applied: Hooke-Jeeves and Nelder-Mead. The metaheuristicalgorithms estimated optimal values of both of the kernel function parameters and the number of components in the KPLS regression.
Cloud computing introduced a new paradigm in IT industry by providing on-demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PM...
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Cloud computing introduced a new paradigm in IT industry by providing on-demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state-of-the-art multiobjective VM placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.
The increasing dependency on network connectivity necessitates superior network performance, characterized by continuous availability and high reliability. Multiprotocol Label Switching (MPLS) networks have emerged as...
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The increasing dependency on network connectivity necessitates superior network performance, characterized by continuous availability and high reliability. Multiprotocol Label Switching (MPLS) networks have emerged as a robust solution to the limitations of traditional IP networks. However, determining optimal Label Switched Paths (LSPs) across multiple domains becomes a complex optimisation problem when considering multiple objectives. This paper addresses this challenge by transforming the path computation into an optimisation task. The Bat Algorithm, a popular meta-heuristic approach, is frequently employed for solving such optimisation problems. Despite its effectiveness, the Bat Algorithm is prone to premature convergence and the local optima problem, leading to sub-optimal solutions. This study explores the influence of the algorithm's loudness parameter and introduces a novel Adjustable Bat Algorithm (ABAT) that dynamically optimizes this parameter. The ABAT is integrated with a Pareto-based approach to enhance the discovery of non-dominant solutions. The proposed method effectively computes optimal paths while identifying the Pareto front of solutions, providing a balanced trade-off among competing objectives. The performance of the proposed algorithm was evaluated on networks of varying scales and compared against the standard Bat Algorithm and other meta-heuristic methods. The evaluation focused on convergence rate and computational complexity, demonstrating that the proposed Adjustable Bat Algorithm outperforms existing methods in both metrics, offering a more robust and efficient solution for multi-objective path optimisation in MPLS networks. Experimental results indicate that the proposed Adjustable Bat Algorithm (ABAT) outperformed the regular Bat Algorithm in terms of convergence rate by up to 35% and reduced load balancing expenses and routing latency by 15% to 20% at various MPLS network scales.
In this paper, a hybrid nature-inspiredmetaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm an...
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In this paper, a hybrid nature-inspiredmetaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators-the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristicalgorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications.
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