Annual Energy Outlook

In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different fields, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist non-dominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SD), Inverted Generational Distance (IGD), Runtime (RT), and Generational Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems.

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A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

To handle the multiobjective optimization problems of truss-bar design, this paper introduces a new metaheuristic multiobjective optimization algorithm The proposed algorithm is based on a recently reported single objective version of the gradient-based optimizer (GBO) inspired by the gradients of Newton’s equations The proposed algorithm is called as multiobjective gradient-based optimizer (MOGBO), in which two operators, such as local escaping operator and gradient search rule, and few vector sets are utilized in the search phase and the elitist non-dominated sorting mechanism is used for agent sorting to find Pareto optimal solutions The proposed MOGBO is a posteriori method, and the traditional crowding distance mechanism is employed to confirm the coverage of the best solutions for the objectives of the given problem The performance of the proposed MOGBO algorithm is verified and validated on different test cases, including 15 unconstraint benchmark test suites and eight constraint multiobjective truss bar design problems To prove the superiority of the MOGBO algorithm, the performance is compared with state-of-the-art algorithms, such as multiobjective ant lion optimization (MOALO), multiobjective water cycle algorithm (MOWCA), multiobjective colliding bodies optimization (MOCBO), and non-dominated sorting gray wolf optimizer (NSGWO) in terms of metrics, such as hyper-volume, coverage, inverted generational distance, pure diversity, Spacing, Spread, coverage Pareto front, diversity maintenance, generational distance, and runtime The solutions obtained by the proposed MOGBO algorithm is highly accurate and requires less runtime than the other selected algorithms The obtained results also show the efficiency of the MOGBO in solving most of all the complex multiobjective problems This research will be further backed up with external guidance for the future research at https://premkumarmanoharanwixsitecom/mysite 

MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems

Butterfly Optimization Algorithm (BOA) is a recently developed metaheuristic search algorithm that mimics the food-search process of the butterflies in nature. The studies reveal that BOA shows significant performance on the non-constrained and unbiased mathematical functions, but in the cases of shifted and rotated functions and/or constrained optimization problems, its search capacity is considerably restricted. To address this shortcoming, the present study deals with developing a new fuzzy decision-making strategy and introducing a new auxiliary concept called “virtual butterfly” to enhance the search capability of the standard BOA. The developed fuzzy strategy permanently monitors the optimization process and tries to adjust each butterfly’s search behavior based on the governing conditions of the current problem. Also, the virtual butterfly concept involving the information of the whole colony tries to provide alternative promising search directions for the other butterflies. The new reinforced method is named Fuzzy Butterfly Optimization Algorithm (FBOA). To evaluate the search performance of the proposed FBOA, it is tested on solving a suit of constrained and non-constrained optimization problems and the achieved outcomes are compared with those given by some other well-established techniques including its parent method (i.e. BOA). The results show that the implemented improvements significantly increase the search capability of the regular BOA, especially in the case of constrained engineering problems.

Enhanced Butterfly Optimization Algorithm with a New fuzzy Regulator Strategy and Virtual Butterfly Concept

This work proposed a new metaheuristic dubbed as Chaotic Lévy flight distribution (CLFD) algorithm, to address physical world engineering optimization problems that incorporate the chaotic maps in the elementary Lévy flight distribution (LFD). Hybridization aims to increase the LFD rate of convergence while also providing a problem‐free optimization approach. The proposed methodology is investigated for five case studies of constrained optimization issues followed by shape optimization of structural design. The outcomes from the CFLD algorithm are further contrasted with its fundamental version and other distinguished recently introduced algorithms. The computational analysis illustrates the dominance of CLFD over other considered optimizers. Moreover, the present investigation shows that CLFD is a robust technique that can efficiently find optimal mechanical design problems with a proper chaotic map selection.

A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems

A novel hybrid optimizer called Multi-Objective Hybrid Heat Transfer Search and Passing Vehicle Search optimizer (MOHHTS–PVS) is proposed while its performance is investigated for the structural design. The HHTS–PVS optimizer combines the merits of Heat Transfer Search (HTS) and Passing Vehicle Search (PVS). The design problem is posed for weight minimization and maximization of nodal deflection subject to multiple constraints of trusses. In the proposed optimizer, HTS acts as the main engine and PVS is added as an auxiliary stage into it to overcome its limitations and enhance the performance while simultaneously creating harmony between global diversification and local intensification of the search. Five challenging structure optimization benchmarks are optimized having discrete design variables. For performance validation, four state-of-the-art optimizers are compared with the proposed optimizer. Pareto Front Hypervolume and Spacing-to-Extent test are performance indicators for all the test examples. HHTS–PVS achieved the best non-dominated Pareto fronts with continuous and well diverse solutions set. The statistical analysis is done by performing Friedman’s rank test and allocating respective ranks to the optimizers. As per the outcomes, it is concluded that HHTS–PVS outperforms other optimizers and simultaneously shows its competency in solving large engineering design problems.

Hybrid Heat Transfer Search and Passing Vehicle Search optimizer for multi-objective structural optimization

The unsteady characteristics of bubble dynamics inside the air reactor within the first 0–40 s of reforming has always been studied for defining the design criteria of the reactor. In the study, a temporal aspect of the hydrodynamics and chemical kinetics in the reactor of a chemical looping reforming system in form of volume fraction contours of solid species and molar fraction of H2O has been numerically simulated by considering manganese (Mn) and iron (Fe) based metal oxides as oxygen carriers. The Finite Volume Method based approach has been employed to simulate the steam reactor model by encompassing it as a fluidized bed reactor. The granular flow under kinetic theory has been employed using a multiphase Eulerian-based approach for both gas and solid phases in the form of a shrinking core model. An influence of various operating parameters such as particle size of the oxygen carriers, steam inlet velocity, and temperature of the steam reactor on an overall conversion rate of iron-based oxide (FeO) and manganese-based oxide (MnO). The maximum steam conversion rate for FeO and MnO was observed at 32% and 34% at 0.6 m/s steam velocity, 48% and 60% at a maximum temperature of 1273 K, and 47% and 64% at a particle size of 100 µm, respectively.

Numerical analysis of multiphase flow in chemical looping reforming process for hydrogen production and CO2 capture

Water distillation by utilising free energy from the sun is one of the significant techniques for getting freshwater from salty and seawater. For the remote areas and small societies where freshwater is distant, solar distillation is one of the best explanations for freshwater creation. The main objective of this study is to evaluate the performance of the flat plate collector–assisted conventional solar still incorporating mirror wall and heat storage material, which was tested at Anchor institute of solar energy studies, Mehsana (23.5275311° latitude and 72.3881041° longitude), Gujarat. Moreover, the study captures average productivity with and without FPC which was 1.5 L and 1.0 L respectively during the day time for the entire period of experiments. In this study, the water depth is varied from 1 to 5 cm inside the single basin solar still to obtain the optimum depth. It was observed that when mirror augmented still was operated with the FPC, 3.6L/day productivity was achieved with 30% instantaneous efficiency, at solar radiation of 1122 W/m2 and ambient temperature of 24 °C. Also, the maximum productivity was observed at a water depth of 3 cm and 4 cm. Moreover, improvements in daily and yearly productivity were observed to be 51.515% and 56.6474% respectively, which were estimated on the basis of with and without FPC. An experiment was performed at Anchor Institute of Solar Energy, Mehsana located at the north part of Gujarat where the average annual rain was comparatively less compared to other regions, so this type of solar still can provide potable water to daily workers who work on site. Furthermore, economic study reveals 0.577 INR/litre cost of distiller output for conventional set-up and 0.477 INR/litre for the FPC assisted set-up.

Performance assessment of conventional solar desalination system in Northern part of Gujarat

Background: The use of solar air heater in drying has been increasing in recent years, because of their simplicity and economic configuration. Methods: Design and testing of the corrugated solar collector have been carried out in the present study. The overall system was designed and tested at Gujarat Power Engineering and Research Institute, Mehsana, India (GPERI, 23◦31'37.4\"N72◦23'14.1\"E). Exergetic efficiency, thermal efficiency, and temperature outlet at the same mass flow rate and solar irradiance were the major parameters for the present study. The designed system was tested by varying the mass flow rate of 0.1075, 0.215, and 0.3225 m3/sec. Findings: It was found that the temperature of the corrugated collector was maximum at 92◦C at solar radiation of 750 W/m2. Whereas thermal efficiency was found to be 65.10% the same radiation. It was depicted from the tests that the corrugated plate performs more efficiently than a flat plate collector due to surface roughness. Applications: It is a good practice in a region where solar radiation is higher all year to implement this type of arrangement for the utilization of any solar gadget, and to enhance its performance efficiency.Novelty: it can be observed that collector outlet temperature in the semi-arid region of Gujarat was maximum at the minimum collector area. This should be the best design and thermal performance in a semi-arid climate.

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Pranav Mehta

Papers

Hybrid Heat Transfer Search and Passing Vehicle Search optimizer for multi-objective structural optimization

Numerical analysis of multiphase flow in chemical looping reforming process for hydrogen production and CO2 capture

Performance assessment of conventional solar desalination system in Northern part of Gujarat

Mathematical modeling and performance assessment of solar air collector