Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

Asaithambi, Sasikumar; Muthaiah, R.

doi:10.1063/1.5020999

Cited by 45 publications

(15 citation statements)

References 21 publications

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“…The transfer function was used to convert SSA into binary system to maximize the classification accuracy and extract the optimal feature set. Literature [18] proposed the application of SSA in electrical engineering. The author applies SSA to Complementary Metal-Oxide-Semiconductor (CMOS) differential amplifier and comparison circuit size optimization.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

2020

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Wind power generation accounts for an increasing proportion of the power grid, so efficient and accurate real-time wind power prediction is particularly important for wind power grid. In view of the strong randomness and fluctuation of wind and the difficulty of predicting wind power, a Salp Swarm Algorithms-Extremely Learning Machine (SSA-ELM) based ultra-short-term wind power prediction model is proposed. In this case, the multi-input sample set is composed of historical wind speed, temperature, wind direction, atmospheric pressure and other climatic factors that are highly correlated with wind power, and the network parameters are determined in the training process. In order to improve the adaptability and accuracy of the prediction model, the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM) are optimized by exploring and developing the Salp Swarm Algorithm in the iterative process. Finally, the simulation experiment is conducted with the actual data of a wind farm in Henan Province, and the comparison with the traditional Extreme Learning Machine, Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) and Back Propagation (BP) neural network model shows that the new method avoids falling into the local extreme value, and has faster convergence speed and higher prediction accuracy. INDEX TERMS Wind power prediction, salp swarm algorithms, extreme learning machine, optimization algorithm.

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Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

2020

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“…In summary, SSA has certain strengths among the above algorithms. Therefore, SSA has been widely used in engineering applications [23], machine learning [24], image processing [25], and many other application fields [26][27]. In SSA, the population consists of a leader and followers.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Singh et al [28] developed a new algorithm called HSSASCA by combining SSA with a sine cosine algorithm (SCA), the performance of HSSASCA is verified in twenty-two standard functions. Asaithambi et al [23] designed a hybrid algorithm based on SSA and Hooke-Jeeves algorithm called SSA-HJ, which is applied for optimizing the sizing of a CMOS differential amplifier and the comparator circuit. As for modified SSA variants, most of them modify the original SSA by adopting different strategies in the above two continuous phases, i.e., LP and FP.…”

Section: Introductionmentioning

confidence: 99%

An Improved Salp Swarm Algorithm With Spiral Flight Search for Optimizing Hybrid Active Power Filters’ Parameters

Zhang

et al. 2020

IEEE Access

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In view of various issues occasioned by harmonic pollution in the power system, the optimization of the filter parameters is of great significance. However, under the system constraints, estimating the filter parameters accurately and reliably is a challenging task. To complete this task, this paper proposes an improved salp swarm algorithm based on the spiral flight search strategy (ISSA-SFS), which rationally integrates the spiral flight search (SFS) strategy, the multiple leader (ML) strategy, and the random learning (RL) strategy with two improve evolution phases: the improved lead phase (ILP) and the improved follow phase (IFP). In the ILP, the SFS strategy is introduced to enhance the global search capability and avoid premature convergence. Furthermore, in the ILP, an ML strategy is proposed to select multiple leaders, further strengthening the global search capability of the proposed algorithm. In the IFP, a simple RL strategy is developed to learn two different random individuals, efficiently improving the local exploitation. The proposed algorithm ISSA-SFS is applied to optimize two prominent hybrid active power filter (HAPF) topologies, and each topology contains four different study cases. The overall experimental results indicate that the ISSA-SFS is a more promising alternative to achieve the optimal design of HAPFs compared with other well-established algorithms, especially in terms of accuracy and robustness. INDEX TERMS Parameter optimization, Harmonic pollution (HP), Hybrid active power filter (HAPF), Salp swarm algorithm (SSA), Spiral flight search (SFS).

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“…In [29]- [32], SSA has been used as a novel optimization engine for wrapper features selection (FS) approaches. Asaithambi and Rajappa [33] used SSA to find the optimal size for the CMOS differential amplifier and comparator circuits. Ekinci and Hekimoǧlu [34] proposed to apply SSA to adjust Power System Stabilizer (PSS) in multimachine power systems.…”

Section: Introductionmentioning

confidence: 99%

Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers

et al. 2019

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Salp swarm algorithm (SSA) is a newly developed meta-heuristic algorithm, which is mainly developed based on the swarming behavior of salps sailing and foraging in the ocean. An improved salp swarm-based optimizer is proposed in this paper to overcome the potential shortcomings of original SSA, including being easily trapped in local or deceptive optima and its slow convergence rates in dealing with some high-dimensional and multimodal landscapes. The designed variant is called CMSSA that combines two strategies simultaneously. First, a chaotic exploitative mechanism with ''shrinking'' mode is introduced into the basic SSA to improve the exploitative tendencies of the algorithm. Then, a combined mutation scheme is adapted to make full use of the strong intensification capabilities of Gaussian mutation and the strong exploratory leanings of Cauchy mutation. In addition, the embedded strategies can achieve a more stable equilibrium between the core searching patterns of the SSA, which are diversification and intensification. We thoroughly studied the optimization advantages of the improved CMSSA using several representative benchmark cases, including unimodal, multimodal, and fixed-dimension multimodal functions, and three well-regarded engineering cases. The obtained experimental results, statistical tests, and comparative simulations indicate that the exploratory and exploitative proclivities of the SSA and its convergence patterns are vividly improved. The results indicate that the proposed CMSSA is a promising algorithm and shows superior efficacy compared with other algorithms.

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Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

Cited by 45 publications

References 21 publications

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

An Improved Salp Swarm Algorithm With Spiral Flight Search for Optimizing Hybrid Active Power Filters’ Parameters

Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers

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