In this paper, some methodologies aimed at the identification of the Pareto front of a multi-objective optimization problem are presented and applied. Three different approaches are presented: local sampling, Pareto front resampling and Normal Bounda...
Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal...
In this paper, we address the problem of obtaining bias-free and complete finite size approximations of the solution sets (Pareto fronts) of multi-objective optimization problems (MOPs). Such approximations are, in particular, required for the fair u...
Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have be...
Multimodal multiobjective optimization problems, characterized by multiple solutions mapping to identical objective vectors, are ubiquitous in real-world applications. Despite their prevalence, most existing multimodal multiobjective evolutionary alg...
This paper presents a method of choosing a single solution in the Pareto Optimal Front of the multi-objective problem of the spectral and energy efficiency trade-off in Massive MIMO (Multiple Input, Multiple Output) systems. It proposes the transform...
With many efficient solutions for a multi-objective optimization problem, this paper aims to cluster the Pareto Front in a given number of clusters K and to detect isolated points. K-center problems and variants are investigated with a unified formul...
This work presents a design for uninterruptible power supply inverters using Pareto front optimization for improved cost and efficiency. Three PWM modulation techniques applied to the full-bridge inverter are analyzed. As a result, the best MOSFET de...
In this paper, we propose a solution for gunshot location in national parks. In Spain there are agencies such as SEPRONA that fight against poaching with considerable success. The DiANa project, which is endorsed by Cabañeros National Park and the SE...
In recent years, value for money (VFM) evaluations using the Public Sector Comparator (PSC) method have gradually been adopted by governments worldwide in the field of public investment, and used as decision-making tools for public–private part...
The article presents the development of new physics-informed evolutionary neural network learning algorithms. These algorithms aim to address the challenges of ill-posed problems by constructing a population close to the Pareto front. The study focus...
Hybrid kinetic models, linking structured models of cell (nano-scale) metabolic processes to the dynamics of macroscopic variables of the bioreactor, are proven to lead to more precise predictions of all key-species dynamics under variable operating...
This work presents a novel approach for multiobjective optimization problems, extending the concept of a Pareto front to a new idea of the Pareto region. This new concept provides all the points beyond the Pareto front, leading to the same optimal co...
In this study, the inductor optimization design is performed by applying the Pareto optimization technique. As environmental problems emerge, the electric vehicle market is expanding, and accordingly, volume reduction and high efficiency of the onboa...
Increasing wastewater treatment efficiency is a primary aim in the circular economy. Wastewater physicochemical and biochemical processes are quite complex, often requiring a combination of statistical and machine learning tools to empirically model...
Studies have shown that the performance of multi-objective evolutionary algorithms depends to a large extent on the shape of the Pareto fronts of the problem. Although, most existing algorithms have poor applicability in dealing with this problem, es...
Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This stud...
Selecting a variant of forest regeneration cuttings that would ensure fulfilling multiple, frequently conflicting forest functions is a challenging task for forest management planning. The aim of this work is to present an efficient and complex analy...
This paper proposes an optimal design method to suppress critical harmonics and improve the power factor by using passive power filters (PPFs). The main objectives include (1) minimizing the total harmonic distortion of voltage and current, (2) minim...
Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for the numerical treatment of multi-objective optimization problems (MOP) during the last three decades. One important task within MOEAs is the archiving (or selection) o...
Decision-making plays a pivotal role in data-driven optimization, aiming to achieve optimal results by identifying the most effective combination of input variables. Traditionally, in multi-objective data-driven optimization problems, decision-making...
In the context of this study, it is focused on the design of cantilever soldier piles under the concept of Pareto optimality with multiobjective analyses of cost and CO2 emission considering the change in the excavation depth, the shear strength para...
This paper compares two multi-objective optimization strategies for spiral-grooved dynamic gas thrust bearings. The first optimization is carried out using an analytical model, which is valid under the assumption of a high number of grooves. The seco...
This paper addresses a biobjective bus routing problem that pays attention to both the routing cost and the total distance walked by the individuals to reach their assigned pickup point. These two objectives are conflicting. Generally, the less the i...
The optimal scheduling of DES is to solve a multi-objective optimization problem (MOP) with complex constraints. However, the potential contradiction between multiple optimization objectives leads to the diversity of feasible solutions, which has a s...
Adaptive weight-vector adjustment has been explored to compensate for the weakness of the evolutionary many-objective algorithms based on decomposition in solving problems with irregular Pareto-optimal fronts. One essential issue is that the distribu...
This article introduces a novel approach for generating low-sensitive Pareto fronts of analog circuit performances. The main idea consists of taking advantage from the social interaction between particles within a multi-objective particle swarm optim...
In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three obje...
This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution syst...
This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air cond...
In recent years, inulin enzymatic hydrolysis has become a very promising alternative for producing fructose on a large scale. Genetically modified chicory was used to extract inulin of industrial quality. By using an adequate kinetic model from the l...
The sensitivity of the elbow rule in determining an optimal number of clusters in high-dimensional spaces that are characterized by tightly distributed data points is demonstrated. The high-dimensional data samples are not artificially generated, but...
This research paper is aimed at a specific group of emergency medical service location problems, which are solved to save people’s lives and reduce the rate of mortality and morbidity. Since searching for the optimal service center deployment i...
This paper introduces a novel quantum-inspired algorithm for numerical multiobjective optimization, uniquely integrating the multilevel structure of qudits with principles of controlled thermonuclear fusion. Moving beyond conventional qubit-based app...
The optimization of power parameters is the key to the design of pure electric vehicles. Reasonable matching of the relationship between various parameters can effectively reduce energy consumption and achieve energy sustainability. In this paper, se...
This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and co...
A novel rolling scheme incorporating an inclined oval-caliber configuration is proposed to enhance plastic deformation mechanisms in the traditional oval–round rolling sequence. Finite Element Method (FEM) simulations were performed using DEFOR...
In this work, we evaluate the impact of numerical integration methods and perturbation models on the computational speed and position accuracy of orbit propagation techniques. With increasing numbers of satellites in orbit, space traffic management m...
A posteriori multiobjective optimization relies on a series of mutually independent single-objective optimization subproblems, which can be run in parallel, thus making full use of a multiprocessor (or multicore) computer. This paper presents a paral...
The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a widely used approach for solving multi-objective radiation shielding optimization problems, but it struggles to distinguish equally ranked solutions near the Pareto front, leading to reduc...
The stability of endoreversible heat engines has been extensively studied in the literature. In this paper, an alternative dynamic equations system was obtained by using restitution forces that bring the system back to the stationary state. The depar...
Since cardiovascular diseases (CVDs) pose a critical global concern, identifying associated risk factors remains a pivotal research focus. This study aims to propose and optimize a fuzzy system for cardiovascular risk (CVR) classification using a mul...
The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most exi...
Improving network security is a difficult problem that requires balancing several goals, such as defense cost and need for network efficiency, to achieve proper results. Modeling the network as a game and using optimization problems to select the bes...
Electric vehicles (EVs) are gradually gaining high penetration in transportation due to their low carbon emissions and high power conversion efficiency. However, the large-scale charging demand poses significant challenges to grid stability, particul...
The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a...
Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often s...
The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and ba...
Multi-objective Bayesian Optimization (MOBO) has become a promising strategy for accelerating process development in Laser Powder Bed Fusion of Metals (PBF-LB/M), where experimental evaluations are costly, and design spaces are high-dimensional. This...
This paper proposes a selection strategy for determining the optimal capacity and location of a static synchronous compensator (STATCOM) device under intermittent output variations of renewable energy sources using a multi-objective cuckoo search (MO...
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