A Brief Review on Multi-objective Differential Evolution

Ayaz, Mohd.; Panwar, Ankita; Pant, Millie

doi:10.1007/978-981-15-0751-9_95

Cited by 10 publications

(3 citation statements)

References 41 publications

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“…It is important to point out that, before implementing the NSGA-II algorithm, two evolutionary algorithms were tested. The evolutionary algorithms used were: The Multiobjective Differential Evolution (MODE) algorithm [18] and the Differential Evolution Multiobjective Optimization (DEMO) algorithm [19]. However, due to the nature of the calorimeter, the two algorithms could not be implemented, because the optimization problem had mixed variables, since one objective function had to choose the insulating material (something tangible), and the other objective function had to choose the physical dimensions of the calorimeter (numbers).…”

Section: Methodsmentioning

confidence: 99%

Design of an Adiabatic Calorimeter for Cementitious Mixtures by Multi-Objective Optimization

Jhonatan A. Becerra-Duitama,

Mauricio Mauledoux,

Óscar F. Avilés

2023

Adv. technol. innov.

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This study aims to design an adiabatic calorimeter for cementitious mixtures using NSGA-II and the Pareto optimal solution set. In this multi-objective optimization, the controller effort and heating time are selected as objective functions. Likewise, the volume and the material to be heated were chosen as decision variables. The optimal solution was selected using Nash bargaining methods. After implementing the optimal solution, the Wilcoxon test was applied to statistically validate the developed work. The measurements performed were compared with other research and it was observed an improvement in the measurement of heat of hydration in cementitious mixtures. Also, it was noted a decrease in the error in the temperature measurement.

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Section: Methodsmentioning

confidence: 99%

Design of an Adiabatic Calorimeter for Cementitious Mixtures by Multi-Objective Optimization

Jhonatan A. Becerra-Duitama,

Mauricio Mauledoux,

Óscar F. Avilés

2023

Adv. technol. innov.

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“…Notably, although DE is an evolutionary algorithm, it lacks a real natural paradigm and is not an exact replica of natural evolution, unlike other evolutionary algorithms. DE has demonstrated outstanding performance in a wide range of optimization problems from diverse scientific domains, including constrained and multi-objective optimization problems [23]. It belongs to the stochastic population-based evolutionary group and, like other evolutionary algorithms, uses a population of candidate solutions and stochastic mutation, crossover, and selection operators to move the population toward superior solutions in the design space.…”

Section: Function Formulamentioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

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This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

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“…Future directions- Besides GA, there are several other metaheuristics like PSO, DE, ACO, ABC, etc., for which the multi-objective variants have been developed and tested. For example: MOPSO [215], MODE [216], [217], MOACO [218] and MOABC [219], MOGWO [220], MOEA\D [221]. A study highlighting the features of different multiobjective algorithms is likely to be quite interesting.…”

mentioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

Self Cite

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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A Brief Review on Multi-objective Differential Evolution

Cited by 10 publications

References 41 publications

Design of an Adiabatic Calorimeter for Cementitious Mixtures by Multi-Objective Optimization

Design of an Adiabatic Calorimeter for Cementitious Mixtures by Multi-Objective Optimization

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

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