An Integer Cat Swarm Optimization Approach for Energy and Throughput Efficient MPSoC Design

Murtza, Shahid Ali; Ahmad, Ayaz; Qadri, Muhammad Yasir; Qadri, Nadia N.; Alhaisoni, Majed; Baloch, S.

doi:10.2991/ijcis.d.200617.001

Cited by 2 publications

(2 citation statements)

References 32 publications

(49 reference statements)

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“…Step 2: Classify each cat randomly into seeking and tracing mode according to the Mixing Ratio (MR) of 0.30 [13]. For example, if 10 cats are generated, 3 cats will go in tracing mode while the remaining 7 cats will go in seeking mode.…”

Section: Table 3 Pseudocode Of Seeking Mode In Csomentioning

confidence: 99%

Cat Swarm Optimization for Sizing Photovoltaic-Battery based Stand-Alone System

Hanta

Sulaiman

Othman

2022

J. Phys.: Conf. Ser.

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Stand-Alone Photovoltaic (SAPV) systems have been a popular mode of electricity generation especially in remote areas which deprived of grid electricity. However, one of the main challenges in the development of such systems is optimal system sizing. An undersized system causes failure of meeting the load demand. Thus, this paper presents Cat Swarm Optimization (CSO) for optimal sizing of an SAPV system. A single objective function was formulated for the sizing optimization problem in which models of system components, i.e. PV module, battery, charge controller and inverter, each represented in integer codes, were transcribed as decision variables whereas the Loss of Power Supply Probability (LPSP) was set as the objective function that needs to be minimized. Prior to the optimization, an Iterative Sizing Algorithm (ISA) was initially developed to produce the optimal sizing solution using non-computational intelligence approach. The solution from ISA served as benchmark for CSO. Later, CSO was used to select the optimal model of PV module, battery, charge controller and inverter such that the LPSP was minimized. The results showed that CSO was able to yield similar LPSP as recommended by ISA. Besides having computation time of approximately 3.65 times lower than ISA, CSO was also found to be superior by producing lower LPSP when compared with Evolutionary Programming (EP).

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Section: Table 3 Pseudocode Of Seeking Mode In Csomentioning

confidence: 99%

Cat Swarm Optimization for Sizing Photovoltaic-Battery based Stand-Alone System

Hanta

Sulaiman

Othman

2022

J. Phys.: Conf. Ser.

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“…Since the time complexity of ILP-based algorithms does not allow them to scale well to larger problem sizes, meta-heuristic algorithms, such as evolutionary and swarm algorithms, are more commonly used today [ 22 ]. They do not make assumptions on the objective functions, and because of that are more flexible, efficient and can find near-optimal solutions in a considerably shorter time [ 16 , 23 , 24 , 25 , 26 , 27 ]. Typically, these algorithms execute many unguided and independent searches from different starting points to increase the chance of reaching global optima.…”

Section: Related Workmentioning

confidence: 99%

Design Space Exploration of Clustered Sparsely Connected MPSoC Platforms

Frid

Sruk

Jakobović

2022

Sensors

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Heterogeneous multiprocessor platforms are the foundation of systems that require high computational power combined with low energy consumption, like the IoT and mobile robotics. In this paper, we present five new algorithms for the design space exploration of platforms with elements grouped in clusters with very few connections in between, while these platforms have favorable electric properties and lower production costs, the limited interconnectivity and inability of heterogeneous platform elements to execute all types of tasks, significantly decrease the chance of finding a feasible mapping of application to the platform. We base the new algorithms on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) meta-heuristic and the previously published SDSE mapping algorithm designed for fully interconnected multiprocessor platforms. With the aim to improve the chance of finding feasible solutions for sparsely connected platforms, we have modified the parts of the search process concerning the penalization of infeasible solutions, chromosome decoding, and mapping strategy. Due to the lack of adequate existing benchmarks, we propose our own synthetic benchmark with multiple application and platform models, which we believe can be easily extended and reused by other researchers for further studying this type of platform. The experiments show that four proposed algorithms can find feasible solutions in 100% of test cases for platforms with dedicated clusters. In the case of tile-like platforms, the same four algorithms show an average success rate of 60%, with one algorithm going up to 84%.

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An Integer Cat Swarm Optimization Approach for Energy and Throughput Efficient MPSoC Design

Cited by 2 publications

References 32 publications

Cat Swarm Optimization for Sizing Photovoltaic-Battery based Stand-Alone System

Cat Swarm Optimization for Sizing Photovoltaic-Battery based Stand-Alone System

Design Space Exploration of Clustered Sparsely Connected MPSoC Platforms

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