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1
Fuzzy optimization with multi-objective evolutionary algorithms: A case study
Published 2007“…On the other hand, an ad hoc Pareto-based multi-objective evolutionary algorithm to capture multiple non dominated solutions in a single run of the algorithm is described. …”
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Integrated Grasshopper Algorithm-Evolutionary Programming Technique for Distributed Energy Resources Allocation
Published 2023Conference Paper -
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Cognitive map approach for mobility path optimization using multiple objectives genetic algorithm
Published 2023“…In this study, multi-objectives genetic algorithm (MOGA) is utilized due to there are more than one objective need to be achieved while planning for the robot moving path. …”
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Multi-objective optimization of stand-alone hybrid renewable energy system by genetic algorithm
Published 2013“…Multi-objective optimization of HRES by Evolutionary Algorithm have become attractive in recent years due to the effectiveness of such algorithms for complicated problems. …”
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Unit commitment in power system using multi-agent evolutionary programming incorporating priority listing optimisation technique / Muhammad Nazree Che Othman
Published 2013“…The search process then being refined using heuristic EP-based algorithm with multi-agent approach to produce the final solution. …”
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Computational intelligence technique for DG installation within contingency scenario / Muhamad Saifullah Mahmud Affandi
Published 2014“…This proposed technique will be compared with Evolutionary Programming (EP) algorithm, that usually designed to maximize or minimize the objective function, which is a measure of the quality of each candidate solution. …”
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Bacterial foraging optimization algorithm for optimal load shedding in power systems / Wan Nur Eliana Afif Wan Afandie
Published 2016“…By using Bacterial Foraging Optimization Algorithm (BFOA) technique, for each types of load shedding, the simulations are tested on IEEE 30-bus system for 3 and 5 load shedding locations. 3 different load conditions and one line outage cases are tested for 3 objective function and 3 multi-objective functions. …”
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8
Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms
Published 2008“…The simulation results indicate that the algorithm is able to segregate and assign the tasks for each scanning head and also able to find the shortest scanning path for different types of objects coordination. …”
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Optimization ofhybrid flow shop scheduling in a machine shop: Achieving energy efficiency and minimizing machine idleness with multi-objective Tiki Taka optimization
Published 2025“…The EE-HFS was optimized using Multi-Objective Tiki Taka Optimization (MOTTA).The study considered machine idle time as a key factor influencing energy efficiency, incorporating it into the scheduling evaluation.The optimization result was compared to established algorithms, such as the Non-dominated Sorting Genetic Algorithm-II, the Multi-ObjectiveEvolutionary Algorithm Based on Decomposition, the Multi-ObjectiveParticle Swarm Optimization,and the recent algorithm,the Multi-ObjectiveGrey Wolf Optimizer. …”
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Optimization of hybrid flow shop scheduling in a machine shop: Achieving energy efficiency and minimizing machine idleness with multi-objective Tiki Taka optimization
Published 2025“…The optimization result was compared to established algorithms, such as the Non-dominated Sorting Genetic Algorithm-II, the Multi Objectives Evolutionary Algorithm Based on Decomposition, the Multi Objectives Particle Swarm Optimization, and the recent algorithm Multi Objectives Grey Wolf Optimizer. …”
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Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning
Published 2025“…It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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New bio-inspired barnacle optimizers based least-square support vector machine for time-series prediction of pandemic outbreaks
Published 2024“…The study demonstrates GBO's superiority over previous algorithms, including BMO and its variants, and other established methods. …”
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15
Real time vein visualization using near-infrared imaging
Published 2020“…Although the concept behind the technique is not complicated, the vein pattern acquisition method and the design and implementation of image processing algorithms become challenging. Nowadays, the major challenge faced by the medical practitioners is the difficulty in accessing subcutaneous veins for intra-venous injections due to various factors like low visibility of vein by naked eyes and patients with too narrow veins. …”
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Development of reliability-centered maintenance model using linguistic failure mode and effect analysis
Published 2019“…Then, a co-evolutionary multi-objective particle swarm optimization (CMPSO) algorithm was applied to handle multi-objective optimization problems and to find the solution sets. …”
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Design of intelligent control system and its application on fabricated conveyor belt grain dryer
Published 2011“…This controller has several advantages over its conventional ANFIS counterpart, particularly the reduction in processing time. Moreover, three evolutionary algorithms (EAs), in particular a real-coded genetic algorithm (GA), a particle swarm optimization (PSO), and a global-best harmony search (GHS), were separately used to train the proposed controller and to determine its scaling factors. …”
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