# Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application

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## Abstract

**:**

## 1. Introduction

## 2. Basic Philosophy of Mono-Switch E Class Inverter

**Assumption 1.**

**Assumption 2.**

**Assumption 3.**

**Scenario-I:**${L}_{o}$ = $2.1{e}^{-13}$ MH, C = $2.4{e}^{-13}$ MF, ${R}_{o}$ = $8.8{e}^{-9}$ M$\Omega $, $\chi $ = $12.8$, and ${f}_{o}$ = $6.067{e}^{-7}$ THz;**Scenario-II:**${L}_{o}$ = $4.3{e}^{-13}$ MH, C = $2.4{e}^{-13}$ MF, ${R}_{o}$ = $3.5{e}^{-7}$ M$\Omega $, $\chi $ = $3.9$, and ${f}_{o}$ = $5{e}^{-7}$ THz.

- (a): $\delta $ = $0.1258$, ${\varphi}_{sn}$ = $0.98$, y = $3.385\times {10}^{-5}$, ${\varphi}_{s}$ = 3,731,886 rad/s, and ${f}_{s}$ = $593.94$ kHz, ${t}_{1}=0.211$ $\mathsf{\mu}$s, ${t}_{2}=1.419$ $\mathsf{\mu}$s;
- (b): $\delta $ = $0.3231$, ${\varphi}_{sn}$ = $8.3\times {10}^{-1}$, y = $6.83\times {10}^{-5}$, ${\varphi}_{s}$ = 2,588,401 rad/s, and ${f}_{s}$ = 2,588,986,505 rad/s, ${t}_{1}=0.7841$ $\mathsf{\mu}$s, ${t}_{2}=1.6431$ $\mathsf{\mu}$s.

## 3. Proposed Adaptive Computing Paradigm

#### 3.1. BWS Biological Lifespan

#### 3.1.1. Breeding Mechanism of BWS-Based Optimization

#### Logical Steps in ABWOA

#### 3.1.2. Adjusting the Configuration

- The proportion of participants who should take part in reproducing is equal to the PS. By regulating the progeny produced, this variable increases diversity and opens up more avenues for inquiry;
- The cannibal function eliminates undesirable members of the colony based on the value of the governing factor, CS. The exploitation stage’s effectiveness can be improved by setting the appropriate level for this option;
- PS denotes the percentage of individuals who mutate. Each variable can direct the search individuals’ movement from the global to the localized phase, propelling it closer to the ideal alternative.

## 4. Simulation and Results

- (a): $\delta $ = $0.1256$, ${\varphi}_{sn}$ = $0.9774$, y = $1.557\times {10}^{-16}$, ${\varphi}_{s}$ = 3,721,985 rad/s, and ${f}_{s}$ = $592.373$ kHz, ${t}_{1}=0.2120$ $\mathsf{\mu}$s, ${t}_{2}=1.4761$ $\mathsf{\mu}$s;
- (b): $\delta $ = $0.3678$, ${\varphi}_{sn}$ = $0.8429$, y = $3.056\times {10}^{-15}$, ${\varphi}_{s}$ = 2,622,943 rad/s, and ${f}_{s}$ = $417.454$ kHz, ${t}_{1}=0.8811$ $\mathsf{\mu}$s, ${t}_{2}=1.5144$ $\mathsf{\mu}$s.

## 5. Practical Validation of Results

## 6. Conclusions

- The discussed inverter can perform optimally in class E at a substantially higher rate than is usually demonstrated in investigations;
- A well-tuned mechanism ensures maximum inverter performance and reduced energy fluctuations at the power-electronic converter;
- The adoption of a modern generational converter, such as a SiC semiconductor, mitigates the need for high voltage demand;
- The proposed tailored ABWOA scheme method enhances the resolution of real-world issues. The advancements in induction cooking pots are promising and can lead to enhanced culinary experiences, increased ease of use, reduced energy consumption, and heightened ecological viability;
- Future research in this field should focus on integrating a greater number of non-linear constraints to attain superior and high-quality optimal solutions.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**MDPI and ACS Style**

Aziz, S.; Lai, C.-M.; Loo, K.H.
Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application. *Mathematics* **2023**, *11*, 3020.
https://doi.org/10.3390/math11133020

**AMA Style**

Aziz S, Lai C-M, Loo KH.
Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application. *Mathematics*. 2023; 11(13):3020.
https://doi.org/10.3390/math11133020

**Chicago/Turabian Style**

Aziz, Saddam, Cheung-Ming Lai, and Ka Hong Loo.
2023. "Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application" *Mathematics* 11, no. 13: 3020.
https://doi.org/10.3390/math11133020