A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization

Zain, Mohamad Zihin bin Mohd; Kanesan, Jeevan; Chuah, Joon Huang; Dhanapal, Saroja; Kendall, Graham

doi:10.1016/j.asoc.2018.06.022

Cited by 73 publications

(20 citation statements)

References 24 publications

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“…Finally, the inclusion of multi-objective figure of merit opens the door to programming protocols embracing targeted performance, as well as additional features, such as power consumption and savings in the use-of-components. These can be optimized with multi-objective optimizers 49 . Regarding the configuration speed, an operation cycle is mainly limited by the time required to measure the desired portion of the scattering matrix.…”

Section: Discussionmentioning

confidence: 99%

Multipurpose self-configuration of programmable photonic circuits

et al. 2020

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Programmable integrated photonic circuits have been called upon to lead a new revolution in information systems by teaming up with high speed digital electronics and in this way, adding unique complementary features supported by their ability to provide bandwidth-unconstrained analog signal processing. Relying on a common hardware implemented by two-dimensional integrated photonic waveguide meshes, they can provide multiple functionalities by suitable programming of their control signals. Scalability, which is essential for increasing functional complexity and integration density, is currently limited by the need to precisely control and configure several hundreds of variables and simultaneously manage multiple configuration actions. Here we propose and experimentally demonstrate two different approaches towards management automation in programmable integrated photonic circuits. These enable the simultaneous handling of circuit self-characterization, auto-routing, self-configuration and optimization. By combining computational optimization and photonics, this work takes an important step towards the realization of high-density and complex integrated programmable photonics.

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Section: Discussionmentioning

confidence: 99%

Multipurpose self-configuration of programmable photonic circuits

et al. 2020

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“…As far as intrinsic evolution of NIRPSO is concerned our institute is also working on an implementation of the reconfigurable sensory self-x system for industry 4.0. Lastly, the proposed NIRPSO will be modified by hybrid evolutionary algorithms for handling multi-objective problem without the need of agglomerative approach anymore [4].…”

Section: Discussionmentioning

confidence: 99%

Self-x integrated sensor circuits immune to measurement noise in the presence of input perturbation by using robust optimization / Integrierte Sensorelektronik mit Self-x-Eigenschaften und verbesserter Immunität gegenüber Rauscheinflüssen bei der Messung und Störung der Eingangsgrößen durch ein robustes Optimierungsverfahren

Zaman

König

2019

Tm - Technisches Messen

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This paper aims to propose a new technique to extend the performance of the reconfigurable self-x sensory system for industry 4.0 to efficiently obtain robust solution even in the presence of uncertainty both in the input and output stage. Variance measure is employed to handle the uncertainty in the input stage or search space. As far as measurement or objective space, uncertainty is concerned archive-based method applied, and it does not demand any additional computational resources. The traditional evolutionary algorithm, i.e., particle swarm optimizer (PSO), has been modified by expanding its selection process with the proposed solutions. The performance of the extended algorithm is undertaken to study on three benchmarking functions in the presence of uncertainties. The extrinsic evaluation of the proposed algorithm is also performed on the Miller operational amplifier, which is a fundamental part of sensory systems for industry 4.0. Drift due to fabrication process tolerances and aging effects of the transistors is modeled as input uncertainty of the operational amplifier, and imperfect observer (sensor or analog to digital converter) is modeled as output uncertainty. The application confirms the worthiness of proposed uncertainty handling algorithm for industry 4.0.

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“…However, some heuristic algorithms such as PSO can solve this problem. This group-based search can achieve diversity and globality of solutions, providing efficient algorithms for solving multi-objective problems [29]- [31].…”

Section: ) Multi-objective Optimizationmentioning

confidence: 99%

Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

Yang

2019

IEEE Access

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In wireless sensor networks (WSNs), collecting data with mobile sinks is an effective way to solve the ''energy hole problem''. However, most of existing algorithms of mobile sinks ignore the load balance of rendezvous nodes, which will significantly shorten the network lifetime. Moreover, most mobile sinks are usually required to visit locations of sensor nodes without taking advantage of their communication ranges. Therefore, this paper proposes an energy-efficient trajectory planning algorithm (EETP) based on multi-objective particle swarm optimization (MOPSO) to shorten the trajectory length of the mobile sink and balance the load of rendezvous nodes. EETP aims to reduce the delay in data delivery and prolong the network lifetime. To shorten the trajectory length of the mobile sink, we design a mechanism to select potential visiting points within communication overlapping ranges of sensor nodes, rather than locations of sensor nodes. Additionally, according to trajectory characteristics of the mobile sink, we design an effective trajectory encoding method that can generate a trajectory containing an unfixed number of visiting points. The simulation results show that the proposed EETP is superior to existing WRP, CB and the MOPSO-based algorithm, in terms of delay in data delivery, network lifetime and energy consumption. INDEX TERMS Mobile sink, MOPSO, load balance of rendezvous nodes, trajectory planning.

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A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization

Cited by 73 publications

References 24 publications

Multipurpose self-configuration of programmable photonic circuits

Multipurpose self-configuration of programmable photonic circuits

Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

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