Performance comparison of five metaheuristic nature-inspired algorithms to find near-OGRs for WDM systems

Bansal, Shonak

doi:10.1007/s10462-020-09829-2

Cited by 24 publications

(7 citation statements)

References 52 publications

(111 reference statements)

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“…In the process of optimizing practical problems, in addition to pursuing accuracy, time is also an essential element [ 32 ]. The time complexity of an algorithm is an important indicator for measuring the algorithm.…”

Section: Proposed Frsamentioning

confidence: 99%

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

Yao

Yuan

et al. 2023

Biomimetics

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The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.

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Section: Proposed Frsamentioning

confidence: 99%

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

Yao

Yuan

et al. 2023

Biomimetics

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“…Examples are genetic algorithm [ 17 ], swarm intelligence [ 18 , 19 ], ant colony optimization [ 20 ], and evolutionary techniques [ 21 ], to name a few. These nature-inspired optimizations are among the approximated and meta-heuristic solutions [ 22 ]. In other words, they explore the search space and find the optimal parameters in non-deterministic and efficient ways.…”

Section: Related Workmentioning

confidence: 99%

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

Lim

2022

Sensors

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The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption.

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“…(23) 163 (18) 1060 (30) 834 (30) 565 (30) 338 (33) 246 (11) 603 (7) 535 (14) 541 (30) 566 (24) 269 (15) Optimal DGs size (location) 973 (25) 542 (17) 766 (24) 788 (13) 1040 (29) 160 (18) 917 (30) 742 (25) 536 (11) 895 (33) 976 (12) 1085 30 Added to that, the computation time and complexity of the problem is discussed for the TSA and ITSA technique as shown in Table 14. In this table, the computational complexity is evaluated based on the big O notation [60]. From this table, a comparable computational time is acquired for both algorithms for the 33-bus test system and the 69-bus test system with slight lower time for the proposed ITSA.…”

Section: Comparative Study Of the Proposed Itsa And Other Techniqumentioning

confidence: 99%

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

Fetouh

Elsayed

2020

IEEE Access

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Simultaneous control and operation of Capacitor Banks (CBs) and Distributed Generators (DGs) with Distribution Network Reconfiguration (DNR) is the most important modern trend for distribution systems performance enhancement. The complexity and variety of the control variables of this problem represent a challenge for distribution systems planners and operators as well as interested researchers. Optimal solution can provides quantitative as well as qualitative power service to satisfy consumers' satisfaction and reduces dissipated energy. Furthermore, performance enhancement of distribution systems is very vital where improving their performance directly affects the performance of transmission and generation systems. For solving the considered problem, an improved Tunicate Swarm Algorithm (ITSA), which imitates the swarming behaviors of the marine tunicates and their jet propulsions during its navigation and foraging procedure, is proposed. In ITSA, Lévy flight distributions are emerged in the traditional Tunicate Swarm Algorithm (TSA), which improves the diversification searching abilities of the TSA and consequently avoids the stagnation possibilities. The proposed ITSA is applied and tested for optimal DNR simultaneously with optimal control of the switched CBs and dispatchable DGs taking into account daily load variations. The ITSA is compared with other techniques on the standard 33-bus, 69-bus and the large-scale 119-bus distribution systems considering different automation scenarios of the distribution systems. The simulation results reveal that significant enhancement in distribution system performance is obtained through the application of the proposed automation process using the ITSA. ITSA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literatures.

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Performance comparison of five metaheuristic nature-inspired algorithms to find near-OGRs for WDM systems

Cited by 24 publications

References 52 publications

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

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