Multiobjective energy efficient street lighting framework: A data analysis approach

Sikdar, Pragna Labani; Kar, Samarjit; Thakurta, Parag Kumar Guha

doi:10.1007/s10489-022-03398-3

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…Next is the performance metrics in multi-objective optimization which are mentioned by Baran [16]. Next is the multiobjective energy efficient street lighting framework, which is a data analysis approach, proposed by Sikdar [17]. In the end, a decision support system for finding the Best Restaurant Using the AHP Method is introduced by Nasution [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective Optimization for Order Assignment in Food Cart Evaluation System

Tunga,

Hossain,

Kar

et al. 2024

Preprint

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The growing trend of busy scheduling in everybody’s lifestyle has evolved the concept of the door-to-door delivery system. So, keeping that in mind and food being an essential commodity of life, we have seen that the food delivery industry has boomed in recent years. However, finding the best quality food at a minimum cost is a problem of interest. It is complicated for any customer to decide where to buy the best food at optimal cost if there is no such rating concept for the food. There is no concrete mechanism to help customers with the recommendation process. In this paper, we have designed a model that would help customers evaluate the various food carts that are serving in different locations and generalize the cost and overall rating based on service and food quality. It would basically work in the concept of minimizing the cost and maximizing the overall rating of the food cart. In order to get food cart evaluations, a multi-objective optimization problem is formulated, which includes mainly three multi-objective evolutionary optimization algorithms, namely NSGAII (Nondominated Sorting Genetic Algorithm II), SPEA II (Strength Pareto Evolutionary Algorithm 2) and IBEA (Indicator-Based Evolutionary Algorithm) for the generation of approximated Pareto solutions of our proposed model. The proposed work contributes to an analytics-based approach, mainly based on evolutionary algorithm solutions, to create a multi-objective food cart evaluation system. We used the Zomato dataset to validate the results for comparison and evaluation purposes.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-objective Optimization for Order Assignment in Food Cart Evaluation System

Tunga,

Hossain,

Kar

et al. 2024

Preprint

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“…Algorithms with the highest degree of convergence on this approximate Pareto front are thus considered the most effective and efficient, while algorithms with greater diversity provide more options for solutions in the search space. To perform statistical analysis on convergence and diversity, three multi-objective evolutionary algorithms and the values of six performance matrices, including Generational Distance (GD), Inverted Generational Distance (IGD), space, spread, Hypervolume (HV), and Maximum Pareto Front Error (MPFE), are evaluated [34], [37]. In this work, the other measures are utilized to quantify diversity while GD and MPFE decide on convergence.…”

Section: Contributionmentioning

confidence: 99%

The Influence of Food Bloggers toward Consumer's Attitude in Restaurant Selection: A Multi-objective Metaheuristic Approach

Tunga,

Pal,

Kar

et al. 2023

Preprint

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An analytical approach for a restaurant recommendation system that uses a food blogger ratings and the cost of food items in the restaurant is proposed by maximizing the restaurant rating and minimizing the overall cost. We construct a multi-objective optimization problem to get restaurant recommendations appropriate for the customer's budget and desired rating. Three evolutionary optimization algorithms, namely Nondominated Sorting Genetic algorithm II (NSGA II), Strength Pareto Evolutionary Algorithm 2 (SPEA 2), and Indicator-Based Evolution Algorithm (IBEA) have been used to identify approximated Pareto solutions for our proposed model. The effectiveness of the algorithms under consideration is given and evaluated against several performance indicators. Using Zomato restaurant data, we compare the results in terms of convergence and diversity, which confirms our suggestion for standardization. The suggested work contributes to an analytical approach based on evolutionary algorithm solutions to create a multi-objective restaurant recommendation system, which goes beyond the scope of previous works. Finally, we present a comparative result analysis with the existing Zomato rating using statistical tools and discuss the results.

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“…Later, its updated version, NSGA-II, was proposed by Deb et al [7], which features a notable diversity-preserving operator and is free of user-defined parameters. Many crucial applications of NSGA-II are found in the literature, such as spectrum sharing networks [46], generation expansion planning [32], fault diagnosis in power systems [62], feature selection for facial expression [59], uncertain two objective shortest path problem [44], open vehicle routing problem [13], and multi-objective energy efficient street lighting framework [58]. In order to facilitate the high computational complexity of the crowded comparison operators in NSGA II, Deb and Jain [8] introduced NSGA III, which is primarily based on a reference point approach in which the decision-maker (DM) tries to find a solution nearer to the predefined reference points.…”

mentioning

confidence: 99%

Uncertain multi-objective multi-route shortest path problem by robust enhanced non-dominated sorting genetic algorithms: Application to emergency medical services

Todkar,

Dhodiya

2024

JIMO

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The shortest path problem has emerged as one of the most significant problems in network optimization with broad applications in the real world. Modeling these real-world applications includes a variety of ambiguous phenomena that can be estimated using human beliefs. In dealing with such uncertainty, Liu's uncertainty theory is considered a legitimate tool. This paper examines the uncertain multi-objective multi-route shortest path problem (UMOMRSPP) for directed graphs. The zigzag uncertain variables are used to represent uncertain parameters. For UMOMRSPP, the expected and optimistic value models are developed, and their corresponding deterministic transformation is provided. The deterministic models are solved using three existing multi-objective genetic algorithms (MOGAs), namely the hybrid genetic algorithm, non-dominated sorting genetic algorithm (NSGA)-II & NSGA-III and two proposed aspiration level (AL)-based algorithms: AL-based NSGA-II and AL-based NSGA-III. An emergency medical services problem is studied as an application in the context of UMOMRSPP. A comparison between the proposed methods and the MOGAs mentioned above is presented. A sensitivity analysis of objective functions regarding the shape parameter and AL is performed. The performance measure coverage is computed to demonstrate the effectiveness of the proposed methods. This study suggests that AL-based NSGA-II & III provide solutions based on the decision-maker's AL and can handle real-world UMOMRSPP effectively with efficient outputs.

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Multiobjective energy efficient street lighting framework: A data analysis approach

Cited by 4 publications

References 41 publications

Multi-objective Optimization for Order Assignment in Food Cart Evaluation System

Multi-objective Optimization for Order Assignment in Food Cart Evaluation System

The Influence of Food Bloggers toward Consumer's Attitude in Restaurant Selection: A Multi-objective Metaheuristic Approach

Uncertain multi-objective multi-route shortest path problem by robust enhanced non-dominated sorting genetic algorithms: Application to emergency medical services

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