For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective opt...
Portfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balan...
Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES) in both isolated-is...
The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from...
In a multi-objective optimization problem, a decision maker has more than one objective to optimize. In a bilevel optimization problem, there are the following two decision-makers in a hierarchy: a leader who makes the first decision and a follower w...
Three-dimensional integrated circuits can significantly mitigate the challenges posed by shrinking feature sizes and enable heterogeneous integration. This paper focuses on the 3D floorplanning problem. We formulate it as a multi-objective optimizati...
For large-scale multi-objective optimization, it is particularly challenging for evolutionary algorithms to converge to the Pareto Front. Most existing multi-objective evolutionary algorithms (MOEAs) handle convergence and diversity in a mutually dep...
Optimizing the location of physical activity spaces (PAS) to ensure health, equity and efficiency has long been an important issue in urban planning. Given the health benefits of urban green spaces (UGS), taking Gongshu District in Hangzhou as a case...
The demand for creating automatic text summarization methods has significantly emerged as a result of the web’s explosive growth in textual data and the challenge of finding re-quired information within this massive volume of data. Multi-docume...
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has t...
The design of linear Support Vector Machine (SVM) classification techniques is generally a Multi-objective Optimization Problem (MOP). These classification techniques require finding appropriate trade-offs between two objectives, such as the amount o...
Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-populat...
The standard covariance matrix adaptation evolution strategy (CMA-ES) is highly effective at locating a single global optimum. However, it shows unsatisfactory performance for solving multimodal optimization problems (MMOPs). In this paper, an improv...
Various dominance structures have been proposed in the multi-objective optimization literature. However, a systematic procedure to understand their effect in determining the resulting optimal set for generic domination principles, besides the standar...
The objectives of flood control, power generation, water supply and ecology for reservoir operation are neither completely coordinated nor completely opposed, and its optimal operation and decision-making is very complicated. This study proposed a me...
The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, in this work, differently from the other works proposed in the litera...
A marine condenser with exhausted steam as the working fluid is researched in this paper. Constructal designs of the condenser are numerically conducted based on single and multi-objective optimizations, respectively. In the single objective optimiza...
Including designer preferences in every phase of the resolution of a multi-objective optimization problem is a fundamental issue to achieve a good quality in the final solution. To consider preferences, the proposal of this paper is based on the defi...
Stochastic optimization approaches benefit from random variance to produce a solution in a reasonable time frame that is good enough for solving the problem. Compared with them, deterministic optimization methods feature faster convergence rates and...
The optimization of grinding is a multi-objective problem characterized by high dimensionality, non-linearity, and complexity. Solving this multi-objective optimization (MOO) problem is one of the most challenging tasks in the field of mechanical eng...
By performing data analysis, statistical approaches are highly welcome to explore the data. Nowadays with the increases in computational power and the availability of big data in different domains, it is not enough to perform exploratory data analysi...
Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously....
Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary d...
Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has o...
The work presents the approaches and software developed for multi-objective optimization of nuclear power structures: the modules for energy planning package MESSAGE intended for modeling purposes of developing nuclear power systems and multi-objecti...
Objective solutions of multi-objective optimization problems (MOPs) are required to balance convergence and distribution to the Pareto front. This paper proposes a multi-objective quantum-inspired seagull optimization algorithm (MOQSOA) to optimize t...
Traditional multi-objective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multi-objective optimization problems (MOPs). In this paper, the hybridization of garden balsam optimization (GBO) is presented to solve...
Optimization techniques aim to identify optimal solutions for a given problem. In single-objective optimization, the best solution corresponds to the one that maximizes or minimizes the objective function. However, when dealing with multi-objective o...
The article addresses an approximate solution to the multi-objective optimization problem for a black-box function of a manufacturing system. We employ the surrogate of the discrete-event simulation model of a batch production system in an analytical...
Optimization is considered as a decision-making process to get the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing obje...
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...
This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail...
The design objectives of the structural parameters of the tractor drive motor are diverse, and the constraints are complex. It is difficult to optimize the overall performance of the unit by using the empirical method and single-objective optimizatio...
Real-world problems such as scientific, engineering, mechanical, etc., are multi-objective optimization problems. In order to achieve an optimum solution to such problems, multi-objective optimization algorithms are used. A solution to a multi-object...
Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominat...
The multi-objective design optimization of water distribution systems (WDS) is to find the Pareto front of optimal designs of WDS for two or more conflicting design objectives. The most popular conflicting objectives considered for the design of WDS...
Multi-objective optimization plays a fundamental role in solving complex engineering design problems characterized by conflicting objectives and nonlinear constraints. In this study, a novel hybrid optimization algorithm, named Multi-objective Lemurs...
In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for multi-objective optimization is proposed to solve multi-objective real-world design problems. In the proposed approach, a design exploration is carrie...
Industrial processes provide several of the products and services required for society. However, each industry faces different challenges from different perspectives, all of which must be reconciled to obtain profitable, productive, controllable, saf...
Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-o...
Ship pipeline selection, as a crucial component of ship pipeline design, is often a time-consuming process due to its high complexity. In this study, the response surface methodology combined with the multi-objective particle swarm optimization algor...
A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with impreci...
Recently, optimization-based energy disaggregation (ED) algorithms have been gaining significance due to their capability to perform disaggregation with minimal information compared to the pattern-based ED algorithms, which demand large amounts of da...
Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to...
Sustainable development can be defined in terms of three main components: ecologically sustainable, economically sustainable, and socially sustainable. Conflicting objectives are involved in sustainable development issues, of which the economic, envi...
In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics imp...
Multimodal multi-objective problems refer to situations where more than one Pareto set in the decision space corresponds to the same Pareto front in the objective space. In this paper, we propose a multimodal multi-objective coati optimization algori...
Green infrastructure (GI) is a contemporary area of research worldwide, with the implementation of the findings alleviating issues globally. As a supplement and alternative to gray infrastructure, GI has multiple integrated benefits. Multi-objective...
This study aims to investigate multi-objective configuration optimization of a hybrid energy storage system (HESS). In order to maximize the stability of the wind power output with minimized HESS investment, a multi-objective model for optimal HESS c...
The shearer drum undertakes the main function of coal falling and loading, and picks distributed on it have a great impact on the performance of the drum. However, few studies have optimized the pick and drum at the same time. In this paper, paramete...
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