battleroyale Optimizer (BRO) is a recently proposed optimizationalgorithm that has added a new category named game-based optimizationalgorithms to the existing categorization of optimizationalgorithms. Both contin...
详细信息
battleroyale Optimizer (BRO) is a recently proposed optimizationalgorithm that has added a new category named game-based optimizationalgorithms to the existing categorization of optimizationalgorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimizationalgorithms in the literature, according to the no-free-lunch theorem, no optimizationalgorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimizationalgorithms: the multi-objective Gray Wolf optimizationalgorithm (MOGWO), the multi-objective particle swarm optimizationalgorithm (MOPSO), the multi-objective artificial vulture's optimizationalgorithm (MOAVAO), the optimizationalgorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimizationalgorithms in most of the benchmark suites and operates competitively with them in the others.
This study explores the use of a recent metaheuristic algorithm called a reptile search algorithm (RSA) to handle engineering design optimization problems. It is the first application of the RSA to engineering design ...
详细信息
This study explores the use of a recent metaheuristic algorithm called a reptile search algorithm (RSA) to handle engineering design optimization problems. It is the first application of the RSA to engineering design problems in literature. The RSA optimizer is first applied to the design of a bolted rim, which is constrained optimization. The developed algorithm is then used to solve the optimization problem of a vehicle suspension arm, which aims to solve the weight reduction under natural frequency constraints. As function evaluations are achieved by finite element analysis, the Kriging surrogate model is integrated into the RSA algorithm. It is revealed that the optimum result gives a 13% weight reduction compared to the original structure. This study shows that RSA is an efficient metaheuristic as other metaheuristics such as the mayfly optimizationalgorithm, battle royale optimization algorithm, multi-level cross-entropy optimizer, and red fox optimizationalgorithm.
In fifth-generation (5G) networks, the deployment of heterogeneous networks (HetNets) with macrocells layered over tiny cells is considered to be a practical solution to handle the growing demand for mobile traffic. E...
详细信息
In fifth-generation (5G) networks, the deployment of heterogeneous networks (HetNets) with macrocells layered over tiny cells is considered to be a practical solution to handle the growing demand for mobile traffic. Even though the deployment of a significant count of small cell base stations (BSs) results in a notable rise in energy consumption. In this manuscript, Energy-Efficient (EE) 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU), using Hybrid online green algorithm-based sleep scheduling and cost-efficient deadline-aware Scheduling algorithm (OGASCDASA), are proposed. Here, the energy efficiency optimization issue is in the downlink of two-tier Heterogeneous Cloud Radio Access Networks (H-CRAN) by lowering micro and pico cells. Hybrid OGASCDASA is proposed for reducing remote radio side energy use when maintaining coverage and Quality of service (QoS) of H-CRAN. At the cloud side Baseband Units (BBU), hybrid Simulated Annealing with the Gaussian Mutation and Distortion Equalization algorithm with battleroyaleoptimization is proposed to reduce the energy consumption of BBU by decreasing the count of BBU servers. The proposed EE-HCRAN-Hybrid OGASCDASA-SAGMDEBROA method attains 20.48%, 27.34%, and 32.24% higher throughput and 28.30%, 17.30%, and 32.94% lower delay compared to the existing models, such as heterogeneous computational resource allocation for NOMA (HCRA-NOMA-GMECS), energy-efficient hierarchical resource sharing in uplink-downlink decoupled NOMA heterogeneous networks (EE-HRA-NOMA-HetNet), energy-aware hierarchical resource management with backhaul traffic optimization in heterogeneous cellular networks (EA-HRM-BTO-HCN), respectively.
Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. optimizationalgorithms benefit from stochastic methods;however, they do not guarantee to ob...
详细信息
Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. optimizationalgorithms benefit from stochastic methods;however, they do not guarantee to obtain the optimal solution. Many optimizationalgorithms have been proposed for solving problems with continuous nature;nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimizationalgorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimizationalgorithm, battleroyaleoptimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimizationalgorithms, namely, Particle Swarm optimization, different versions of Genetic algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut.
暂无评论