Concept Of Multi-objective Optimization Research Articles

Multi-objective optimization of a photovoltaic/wind/diesel pumping system with water tank in the Adrar region

Irrigating the remote regions of southern Algeria, which generally use diesel generators, with clean, environmentally-friendly energy is a challenge. Taking into account the solar and wind potential of the Adrar region, a renewable system using both sources seems more cost-effective. The realization of hybrid renewable energy systems takes into account several technical, economic and environmental aspects. This study consists in finding an efficient and adequate energy supply for the irrigation of one hectare of date palm in the Adrar region. It proposes a mathematical modelling for the given problem in different possible models, for this, we used the basic concepts of multi-objective optimization (bi-objective), and the adaptation of the concept of genetic algorithms to find solutions. The theoretical foundations discussed in this research have enabled us to design and implement a software that optimizes the hybrid pumping system used for the irrigation of date palms by minimizing the total cost and reducing CO2 emissions produced by the diesel group.

Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway

Throughout development, complex networks of cell signaling pathways drive cellular decision-making across different tissues and contexts. The transforming growth factor β (TGF-β) pathways, including the BMP/Smad pathway, play crucial roles in determining cellular responses. However, as the Smad pathway is used reiteratively throughout the life cycle of all animals, its systems-level behavior varies from one context to another, despite the pathway connectivity remaining nearly constant. For instance, some cellular systems require a rapid response, while others require high noise filtering. In this paper, we examine how the BMP-Smad pathway balances trade-offs among three such systems-level behaviors, or “Performance Objectives (POs)”: response speed, noise amplification, and the sensitivity of pathway output to receptor input. Using a Smad pathway model fit to human cell data, we show that varying non-conserved parameters (NCPs) such as protein concentrations, the Smad pathway can be tuned to emphasize any of the three POs and that the concentration of nuclear phosphatase has the greatest effect on tuning the POs. However, due to competition among the POs, the pathway cannot simultaneously optimize all three, but at best must balance trade-offs among the POs. We applied the multi-objective optimization concept of the Pareto Front, a widely used concept in economics to identify optimal trade-offs among various requirements. We show that the BMP pathway efficiently balances competing POs across species and is largely Pareto optimal. Our findings reveal that varying the concentration of NCPs allows the Smad signaling pathway to generate a diverse range of POs. This insight identifies how signaling pathways can be optimally tuned for each context.

An adaptive evolutionary multi-objective clustering based on the data properties of the base partitions

Evolutionary multi-objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi-objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi-objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap; and (2) define an offline selection of objective functions and parameter settings of the multi-objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real-life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real-life datasets and 7% in artificial datasets.

Uma Introdução a Metodologias Fuzzy para Otimização Multiobjetivo em Ambiente Incerto

This paper provides an introduction about multiobjective optimization concepts in uncertain environment with the aid of the Fuzzy Mathematical Programming paradigm. The goal is to present methodologies based on Fuzzy Mathematical Programming to solve Multiobjective Programming problems, which model optimization problems with several objective functions and, in many cases, conflicting ones. The article also presents an extensive survey of the classic bibliography in this area, such as the Evolutionary Algorithms for Multi-Objective Optimization (MOEAs), which presents a growing development of algorithms and applications in practical problems since the beginning of this century. Besides, one of the steps in modelling practical problems is to deal with the uncertainty in the real-world data and here we show some proposals that use the Fuzzy Sets Theory for this treatment.

MCDM, EMO and Hybrid Approaches: Tutorial and Review

Most of the practical applications that require optimization often involve multiple objectives. These objectives, when conflicting in nature, pose both optimization as well as decision-making challenges. An optimization procedure for such a multi-objective problem requires computing (computer-based search) and decision making to identify the most preferred solution. Researchers and practitioners working in various domains have integrated computing and decision-making tasks in several ways, giving rise to a variety of algorithms to handle multi-objective optimization problems. For instance, an a priori approach requires formulating (or eliciting) a decision maker’s value function and then performing a one-shot optimization of the value function, whereas an a posteriori decision-making approach requires a large number of diverse Pareto-optimal solutions to be available before a final decision is made. Alternatively, an interactive approach involves interactions with the decision maker to guide the search towards better solutions (or the most preferred solution). In our tutorial and survey paper, we first review the fundamental concepts of multi-objective optimization. Second, we discuss the classic interactive approaches from the field of Multi-Criteria Decision Making (MCDM), followed by the underlying idea and methods in the field of Evolutionary Multi-Objective Optimization (EMO). Third, we consider several promising MCDM and EMO hybrid approaches that aim to capitalize on the strengths of the two domains. We conclude with discussions on important behavioral considerations related to the use of such approaches and future work.

A versatile optimization framework for sustainable post-disaster building reconstruction

This paper proposes an optimization framework for sustainable post-disaster building reconstruction. Based on mathematical optimization, it is intended to provide decision makers with a versatile tool to optimize building designs and to explore the trade-off between costs and environmental impact (represented by embodied energy) of alternative building materials. The mixed-integer nonlinear optimization model includes an analytical building model that considers structural and safety constraints and incorporates regional building codes. Using multi-objective optimization concepts, Pareto-optimal designs are computed that represent the best trade-off designs from which a decision maker can choose when they take additional criteria into consideration. As a case study, we consider the design of a multi-room one-story masonry building in Nepal. We demonstrate how the framework can be employed to address a variety of questions, such as the optimal building design and material selection, the sensitivity of the decision to material prices, and the impact of regional safety regulation thresholds.

A novel strategy to enhance the quality of service (QoS) for data center traffic in elastic optical networks

Abstract Elastic optical networks (EONs) offer tremendous benefits to deal with the exponential increase of the data center traffic. The granularity offered in spectrum allocation supports efficient management of available bandwidth and accommodates multiple traffic to be routed through common links. However, this brings the inherent challenges of routing and spectrum allocation (RSA) constraints. This becomes more complex for elastic optical data center networks (EODCNs), wherein multiple requests arrive at the same time, requiring identical or different bandwidths and each request may have the same or different destination and paths. Also, data requested by different users could be of varying importance levels. Under such a scenario, maintaining the quality of service (QoS) by minimizing the probability of traffic failure and bandwidth blocking is a major task for service providers. To address these problems, we propose an enhanced methodology using path prediction and link-state analysis for efficient allocation of frequency slots and reuse of bandwidth for data centers connected through EONs. Our proposed strategy intents to minimize the number of blocked requests due to non-availability of resources and reduce the failure probability. We introduce here the concept of connectivity degree and Kuhn-Munkres multi-objective optimization for spectrum allocation. We also evaluate the call request blocking probability varying the number of data centers and traffic load. The obtained results show that the proposed algorithm is highly effective in reducing the traffic failure and blocking probability for EODCNs.

Case Article—Pediatrician Scheduling at British Columbia Women’s Hospital

This article describes an in-class role-playing exercise, as well as a case study, on the application of mixed integer programming to help a hospital with physician scheduling. The intended audiences are graduate students or advanced undergraduate students taking a first course in optimization who have been introduced to integer programming. The role-playing exercise aims to develop students’ skills in the iterative process of listening to decision makers describe their problem, asking them questions, and developing initial formulations of the problem. The case study provides students the opportunity to spend more time developing a full mathematical formulation, solving it, and writing up their findings. The case assumes students have already been introduced to the “Big-M” method but assumes no prior introduction to the concepts of hard versus soft constraints. There is no natural objective in this problem, such as the usual “maximize profit” or “minimize cost”; instead, students are introduced to the topic of Goal Programming, which also introduces them to the concept of multiobjective optimization. Supplemental Material: Data are available at https://doi.org/10.1287/ited.2021.0266ca . The Teaching Note is available at https://www.informs.org/Publications/ Subscribe/Access-Restricted-Materials .

Exact algorithms for multiobjective linear optimization problems with integer variables: A state of the art survey

AbstractWe provide a comprehensive overview of the literature of algorithmic approaches for multiobjective mixed‐integer and integer linear optimization problems. More precisely, we categorize and display exact methods for multiobjective linear problems with integer variables for computing the entire set of nondominated images. Our review lists 108 articles and is intended to serve as a reference for all researchers who are familiar with basic concepts of multiobjective optimization and who have an interest in getting a thorough view on the state‐of‐the‐art in multiobjective mixed‐integer programming.

Fuzzy Approach for Bi-Level Programming Problems

Bi-level programming problem (BLPP) can make the final decision result is a mutually coordinated scheme among all layers, which can solve complex practical problems well. This paper summarizes and describes the methods to solve the bi-level programming problem, and proposes a fuzzy method to solve the BLPP by using the concepts of membership function and multiobjective optimization to solve the bilevel programming problem, and gives a numerical example to solve it. It also points out that developing a method to solve fuzzy problems and new concepts to solve multi-level programming problems are practical directions for future research.

Multiobjective optimization for the optimal heat pipe working parameters based on Taguchi's design of experiments

AbstractA heat pipe (HP) is a device transferring large quantities of heat through a small area of the cross‐section with very small deviations in temperature. The thermal impedance of HP is lower while thermal conductance is higher. HPs are designed for controlling temperature, amplification of heat flux, and diminution. HPs are being used in the cooling of aircraft and electronics, solar energy, systems of heat recovery, and nuclear reactors. Complex mathematical formulation demands experimentation to acquire the physical phenomena. Performing experiments is a tedious task with increasing the working parameters and their assigned levels. In such situations, a systematic statistical approach (viz., the Taguchi method) has to be adopted to minimize the number of experiments and to provide the information for the full factorial design of experiments. This paper adopts the modified Taguchi method and applies a simple and reliable multiobjective optimization concept to determine the optimal HP working parameters (viz., heat input, inclination angle, and flow rate). In the optimization process, efficiency, thermal resistance, and overall heat transfer coefficient are the performance indicators (PIs). Empirical relations are developed and validated for the PIs in terms of the HP working parameters. The recommended Taguchi's orthogonal array to perform a few tests may not have the set of optimal working parameters. Additional tests are to be performed to confirm the estimates of the PIs for the optimal working parameters. Confirmation test results in the present study indicate close‐to/within the estimated range.

Multi-objective optimization of micro co-generation spark-ignition engine fueled by biogas with various CH4 / CO2 content based on GA-ANN and decision-making approaches

After the advances in biogas upgrading techniques, the optimization of power and heat generation engines fueled by biogas with varying compositions has become a necessity. Therefore, the aim of the present study is to investigate the necessity of upgrading biogas, mainly CO2 removal, in order to generate power and to determine the optimal operating parameters of a spark-ignition (SI) engine fueled by upgraded biogas. Thus, we propose to couple the artificial neural network technique, multi-objective optimization concept and decision-making approaches in order to improve the performance of a stationary SI engine and to reduce its nitrogen oxides (NOx) emissions while keeping the level of biogas upgrading as low as possible. The brake power (BP), brake thermal efficiency (BTE), and NOx emissions of a SI engine were predicted by using artificial neural network models. The spark ignition timing (IT), biogas CH4 concentration, and throttle valve opening (TVO) were taken as independent variables. The artificial neural network models were trained by using the genetic algorithm (GA) to achieve the best weights and biases. The data used to train and test the ANN models came from experimental tests carried out at constant engine speed (1500 rpm) and air-fuel ratio (λ = 1.4). In the optimization part, the BP, BTE, NOx emissions, and CH4 concentration were all taken as objective functions in order to achieve better performance while lowering NOx emissions and biogas upgrading level. In addition, the optimum operational parameters were obtained by using decision-making approaches. The final optimal point, for the best compromise between high engine performance and low NOx emissions, shows that there is no need to enhance biogas upgrading (lowering CO2 content) in order to generate power and heat. As a result, a SI engine under full load fueled by cleaned raw biogas is the best option.

Conversion of food waste to energy: A focus on sustainability and life cycle assessment

Rapid industrialization and population growth have resulted in the wastage of significant quantity of food globally throughout the value chain right from harvesting to storage, processing and consumption. Normally, these waste materials are allowed to decay in the natural process or burnt to recover a part of the energy in the thermal form, leading to environmental pollution and loss of value. There is an imperative need to realize that food waste is a bundle of energy, which requires carefully planned recovery without damaging the environment. Conversion of food waste to bio-based liquid or gaseous fuels appears to be an attractive option to meet the escalating demand for fuel and at the same time slowing down the fast-depleting fossil fuel resources. In this context, it is felt appropriate to review the current status of the available technologies which are used in the disposal of food waste in order to identify the variables for process intensification to convert them to fuel keeping in mind the environmental concerns and logistics of utilization. The technologies including incineration, landfill, composting, anaerobic digestion, pyrolysis and biochemical methods have been assessed alongside the recent developing technologies such as hydrothermal carbonization and supercritical water gasification. The critical evaluations of these technologies have been made based on the concepts of life cycle analysis, multi-objective optimization and circular bio-economics which help in assessing the environmental impact. Finally, safety aspects and the way forward are highlighted for future work. This article could form a promising path towards a holistic assessment of efficient food waste to energy conversion for a sustainable development.

A multi-objective genetic algorithm approach to design optimal zoning systems for freight transportation planning

This paper contributes to the existing research on freight transportation, spatial and land use planning by investigating an improved spatial aggregation technique to delineate desirable freight traffic analysis zones. Zoning is a process of spatially aggregating several predefined basic spatial units (BSUs) into multiple zones. It plays a vital role in the transportation planning and decision-making process and is well-documented as the modifiable areal unit problem (MAUP). MAUP involves aggregating BSUs to obtain optimal zones satisfying specific criteria and constraints. This paper proposes an improved spatial aggregation methodology to develop a freight traffic analysis zone system by applying the multiobjective optimization concept using a genetic algorithm. The decision variables, namely, (i) Freight trip density; (ii) Number of establishments; (iii) Employment density; and (iv) Compactness, are chosen to represent the elements of freight, passenger traffic, and land use. The problem is formulated as a multiobjective network partitioning problem. The four objectives aim to create zones with better homogeneity and compactness. It is solved using a genetic algorithm with a weighted distance metric approach to prioritize the four objectives. Results show that zones resulting from the improved methodology are superior to the existing zones in terms of homogeneity of decision variables and compactness. The findings are expected to help the decision-making process of urban, transportation, and land-use planners in selecting appropriate freight traffic zone delineation strategies for a given region.

DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework

The emergence of social media platforms like Twitter has become a prominent communication source in disaster outbreak. NGOs, Government agencies leverage twitter’s open and public features to provide immediate relief. Nevertheless, situational information gets immersed in millions of tweets with varying characteristics. Examining each tweet can be cumbersome and time-consuming. Thus, the efficient extraction of disaster-related situational tweets and getting information from all the extracted tweets is required. In the current paper, we have developed a novel framework that uses a deep learning-based classification model to separate the situational tweets from others and summarize them in real-time. Our system is a three-phase process: (a) Creating tweet clusters using a representative set of tweets from the initial set of extracted tweets using a multi-objective optimization concept; (b) When a new tweet arrives, the clusters are updated. The new tweet is classified as situational vs. non-situational. If situational, it is assigned to the closest cluster or new cluster. This assignment is based on its weighted average of syntactic and semantic distances and relevancy to the cluster; (c) Summary is formulated by extracting tweets from each cluster. The proposed approach’s superior performance on four datasets related to different disaster-related events indicates the developed framework’s efficiency over the state-of-the-art techniques.

Ensembling of Gene Clusters Utilizing Deep Learning and Protein-Protein Interaction Information.

Cluster ensemble techniques aim to combine the outputs of multiple clustering algorithms to obtain a single consensus partitioning. The current paper reports about the development of a cluster ensemble based technique combining the concepts of multiobjective optimization and deep-learning models for gene clustering where some additional protein-protein interaction information are utilized for generating the consensus partitioning. The proposed ensemble based framework works in four phases: (i) filtering out the irrelevant genes from the microarray dataset: only the statistically significant genes are considered for further data analysis; (ii) generation of diverse base partitionings: a multi-objective optimization-based clustering technique is proposed which simultaneously optimizes three different cluster quality measures and generates a set of partitioning solutions on the Pareto optimal front; (iii) generation of a consensus partitioning: mentha scores, calculated by accessing a highly enriched protein-protein interaction archive named mentha, of different clustering solutions are considered for generating a weighted incidence matrix; (iv) finally, two approaches are used to generate a consensus partitioning from the obtained incidence matrix. The first approach is based on a traditional machine learning method, and another approach exploits the graph partitioning algorithm and two deep neural models to generate the final clustering. To validate the efficacy of the proposed ensemble framework, it is applied on five gene expression datasets. We present a comparative analysis of the proposed technique over different clustering algorithms in terms of biological homogeneity index (BHI) and biological stability index (BSI). The traditional approach attains an average 3 and 2 percent improvements over the best non-dominated solution with respect to BHI and BSI, respectively, whereas deep learning models illustrate an average 6.8 and 1.5 percent improvements over the proposed traditional approach with respect to BHI and BSI, respectively. Subsequently, Welch's t-test is executed to prove that the results obtained by the proposed methods are statistically significant. Availability of data and materials: https://github.com/sduttap16/DeepEnsm.

Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.

A framework for multi-objective optimization of virtual tree pruning based on growth simulation

We present a framework for multi-objective optimization of fruit tree pruning within a simulated environment, where pruning is performed on a virtual tree model, and its effects on tree growth are observed. The proposed framework uses quantitative measures to express the short-term and long-term effects of pruning, for which potentially conflicting optimization objectives can be defined. The short-term objectives are evaluated on the pruned tree model directly, while the values of long-term objectives are estimated by executing a tree growth simulation. We demonstrate the concept by using a bi-objective case, where the estimated light interceptions of the pruned tree in the current and the next season are used to define separate optimization objectives. We compare the performance of the multi-objective simulated annealing and the NSGA-II method in building the sets of non-dominated pruning solutions. The obtained Pareto front approximations correspond to diverse pruning solutions that balance between optimizing either objective to different extents, which indicates a potential for new applications of the multi-objective pruning optimization concept.

Parallel U-shaped assembly line balancing with adaptive MOEA/D hybridized with BBO

ABSTRACTThe mixed-model line balancing problem of a parallel U-shaped assembly line (PUAL) is addressed in this paper. Four objectives (so-called many objectives) conflicting in nature were concurrently optimized to minimize the (i) task unrelatedness, (ii) different workloads between workstations, (iii) number of stations and (iv) number of workstations. The solution technique adopts the recently emerged and innovative concept of multi-objective evolutionary optimization based on decomposition (MOEA/D), which partitions the fundamental problem into several scalar optimization subproblems and then optimizes them simultaneously. To further strengthen the algorithm, the platform of the MOEA/D was hybridized with the biogeography-based optimization (BBO) algorithm and the adaptive mechanism was also embedded, namely AMOEA/D-BBO. The performances in Pareto sense of AMOEA/D-BBO were evaluated on various problem sizes in comparison with the MOEA/D-BBO, MOEA/D and BBO algorithms. The experimental results demonstrated that AMOEA/D-BBO outperforms the other algorithms in terms of the convergence rate and spread.

A new patterning technique of membrane structures for minimizing deviations of stress and shape

Abstract For cutting pattern generation of membrane structures, non-developability of doubly curved surfaces can cause deviations of stress and shape, which directly influence bearing capacity and structural integrity of final structures. Thus, cutting errors should be diminished as much as possible for precise-forming of membrane structures, especially for airships and air-supported structures as formwork. This paper proposes a new patterning technique to generate the optimized cutting pattern, which is based on a multi-objective optimization concept for simultaneously minimizing the deviations of stress and shape from design. Using the coordinates of 2D elements as key variables, the proposed method performs a geometrically nonlinear finite element analysis based on the initial cutting pattern iteratively. This approach can effectively reduce the deviations of stress and shape of the whole structure by reducing the stress deviation of 2D strips. The validity of the method is examined by the comparison of numerical results, which shows that the actual equilibrium stress is closer to design stress than that of existing works, and the shape distortion is almost negligible.