Application Of Multi-objective Optimization Research Articles

Optimization models for sustainable insect production chains

Abstract Insect value chains are a complex system with non-linear links between many economic, environmental, and social variables. Multi-objective optimization (MOO) algorithms for finding optimal options for complex system functioning can provide a valuable insight in the development of sustainable insect chains. This review proposes a framework for MOO application that is based on gradual implementation, beginning with factors that have an immediate impact on insect production (feed qualities, resource utilization, yield), and progressing to integrated units (environmental, social, and economic impacts). The review introduces the key hotspots of insect production chains, which have been developed in suitable MOO objectives. They represent aspects of resource use, feed quality and its conversion by insects, labor safety and wage fairness, as well as environmental impacts. The capacity of the suggested MOO framework to describe all facets of sustainability may have certain limits. To determine the framework’s applicability and the specific MOO algorithms that can perform the function, modeling and further testing on real insect production chains would be necessary for the intended objectives.

ANN-based Lagrange optimization for RC circular columns having multi-objective functions

Structural engineers must encounter multi-objective optimization (MOO) problems in designs including a design of reinforced concrete (RC) columns, where several design objectives must be satisfied to meet contractors’ interests and produce a sustainable design. MOO applications in structural engineering practice are uncommon despite their high demand. Multi-objective population methods have been mainly investigated. However, previous studies have mostly neglected the stopping criteria and convergence of these algorithms. This study proposes a novel gradient-based algorithm - ANN-based Lagrange optimization and its implementation in designs of circular reinforced concrete columns where three objectives, such as cost, CO2 emissions, and column weight, are minimized simultaneously. This study integrates each single-objective function extracted from an artificial neural network (ANN) into a global Lagrange function, a unified function of objectives (UFO), using the tradeoff-fractions. Lagrange multiplier method is adopted to deal with constrained conditions by using Newton–Raphson iteration to solve Karush-Kuhn-Turker (KKT) conditions. The proposed algorithm brought in a set of optimal results capturing multiple objectives, known as a Pareto frontier, which is compared with those obtained using the Nondominated Sorting Genetic Algorithm – II (NSGA-II), showed that two algorithms calibrated to each other. Overall, the ANN-based Lagrange optimization algorithm exhibited better convergence than NSGA-II.

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of the 5 steps to create, solve, and then select the optimum result. Unlike single-objective optimization, the fifth step of selection or ranking of solutions is often overlooked by the authors of papers dealing with MOO applications. It is necessary to undertake a multi-criteria analysis to choose the best solution. A review of the recent publications using MOO for chemical process engineering problems shows a doubling of publications between 2016 and 2019. MOO applications in the energy area have seen a steady increase of over 20% annually over the last 10 years. The three key methods for solving MOO problems are presented in detail, and an emerging area of surrogate-assisted MOO is also described. The objectives used in MOO trade off conflicting requirements of a chemical engineering problem; these include fundamental criteria such as reaction yield or selectivity; economics; energy requirements; environmental performance; and process control. Typical objective functions in these categories are described, selection/ranking techniques are outlined, and available software for MOO are listed. It is concluded that MOO is gaining popularity as an important tool and is having an increasing use and impact in chemical process engineering.

A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization

Multi-objective optimization (MOO), which involves more than one conflicting objective to be optimized simultaneously, is expected to provide efficient and comprehensive reservoir management (RM) solutions. The multi-objective production optimization problems are considered to be expensive due to the difficulties and cost of operations. Surrogate-assisted evolutionary algorithms (SAEAs), which has proved to be an effective way to solve expensive problems, design computationally cheap function to approximate each objective function. Meanwhile, the optimization process involves a large number of decision variables. However, building a high-quality surrogate model has become difficult due to the “curse of dimensionality”. Base on characterization, an efficient multi-objective optimization framework called SA-RVEA-PCA is proposed to effectively deal with large-scale and computationally expensive simulation-based optimization problems, including three parts:1) Given a set of simulation results, a Gaussian process (GP) model based on Principal Component Analysis (PCA) for each objective function is trained so that the surrogate models can guide the optimization more accurately. 2) A reference vector guided evolutionary algorithm (RVEA) recently developed is employed as a multi-objective optimizer. 3) The information of uncertainty given by GP and angle-penalized penalized (APD) proposed in RVEA are used to update the surrogate models. To the best of our knowledge, the proposed algorithm is applied to a benchmark function, and two typical applications of MOO with synthetic reservoir models. Results show that the proposed method can provide more comprehensive and efficient RM with a higher convergence speed.

Multi-objective optimal design for flood risk management with resilience objectives

In flood risk management, the divergent concept of resilience of a flood defense system cannot be fully defined quantitatively by one indicator and multiple indicators need to be considered simultaneously. In this paper, a multi-objective optimization (MOO) design framework is developed to determine the optimal protection level of a levee system based on different resilience indicators that depend on the probabilistic features of the flood damage cost arising under the uncertain nature of rainfalls. An evolutionary-based MOO algorithm is used to find a set of non-dominated solutions, known as Pareto optimal solutions for the optimal protection level. The objective functions, specifically resilience indicators of severity, variability and graduality, that account for the uncertainty of rainfall can be evaluated by stochastic sampling of rainfall amount together with the model simulations of incurred flood damage estimation for the levee system. However, these model simulations which usually require detailed flood inundation simulation are computationally demanding. This hinders the wide application of MOO in flood risk management and is circumvented here via a surrogate flood damage modeling technique that is integrated into the MOO algorithm. The proposed optimal design framework is applied to a levee system in a central basin of flood-prone Jakarta, Indonesia. The results suggest that the proposed framework enables the application of MOO with resilience objectives for flood defense system design under uncertainty and solves the decision making problems efficiently by drastically reducing the required computational time.

The application of multi-objective optimization method for activated sludge process: a review.

The activated sludge process (ASP) is the most generally applied biological wastewater treatment approach. Depending on the design and specific application, activated sludge wastewater treatment plants (WWTPs) can achieve biological nitrogen (N) and phosphorus (P) removal, besides the removal of organic carbon substances. However, the effluent N and P limits are getting tighter because of increased emphasis on environmental protection, and the needs for energy conservation as well as the operational reliability. Therefore, the balance between treatment performance and cost becomes a critical issue for the operations of WWTPs, which necessitates a multi-objective optimization (MOO). Recent studies in this field have shown promise in utilizing MOO to address the multiple conflicting criteria (i.e. effluent quality, operation cost, operation stability), including studying the ASP models that are primarily responsible for the process, and developing the method of MOO in the wastewater treatment process, which facilitates better optimization of process performance. Based on a better understanding of the application of MOO for ASP, a comprehensive review is conducted to offer a clear vision of the advances, and potential areas for future research are also proposed in the field.

Multi-objective optimization using MS Excel with an application to design of a falling-film evaporator system

There are a number of multi-objective optimization (MOO) methods and programs, as well as many applications of MOO in food engineering. However, almost all these applications have been done using programs/platforms that are not readily used in the industry. On the other hand, engineers are familiar with MS Excel and use it in both research and industrial practice. Hence, in this work, an Excel-based MOO (EMOO) program is developed based on the elitist non-dominated sorting genetic algorithm (NSGA-II) and tested on benchmark problems. It is then applied for MOO of design of a falling-film evaporator system, consisting of a pre-heater, evaporator, vapor condenser and steam jet ejector, for milk concentration. The EMOO program gave well-distributed Pareto-optimal solutions for the MOO problems tested. Design equations and results for two bi-objective optimization problems are presented and discussed.

A Progressive Approach for Multi-objective Design through Inter-related Modeling of Value System and Meta-model

Multi-objective optimization (MOP) has been increasingly interested in supporting agile and flexible manufacturing in complex and global decision environment under diversified customer demands. To avoid stiffness and shortcomings of the conventional methods and to facilitate wide application of MOP in designing tasks, we proposed a flexible approach in this paper. It relies on the progressive modeling of intent or value system and the artificial system in an inter-related manner. The value system is modeled based on easy subjective judgments regarding preference on the alternatives while the system model involving a meta-model is described as responsive surface by the virtue of design of experiments. Beginning with building rough model for each, the approach can lead to the preferentially optimal solution gradually through correcting both models especially concentrated on the niche around the tentative solution. We have shown such approach can improve the complex and complicated design process while reducing the designer's load to express his/her preference. After explaining the proposed method in detail, effectiveness is examined through illustrative case studies.

Theory and application of multiobjective optimization in China — A survey

The study of multiobjective optimization (MOO) in China began at the beginning of the seventies, when some researchers in Beijing and Shanghai adopted techniques in this field to deal with practical problems arising from industrial designs and management sciences for achieving good results. Subsequently, theoretical studies began. A seminar on MOO was organized by members of the Institute of Mathematics of the Chinese Academy of Sciences in 1977. Meanwhile, studies of the theory and applications of MOO were developed in many universities. Related seminars were held and courses were offered to graduate students at these universities. At the same time, many managers and industrial designers had tried to use the techniques of this branch of OR to solve practical problems arising in a variety of fields. In 1981, 1984, 1987 and 1989, national symposiums on MOO were held, each of which had approximately 100 participants; about 300 papers and reports were presented. Up to the present time, about 200 papers on MOO by Chinese scholars have been published in China or abroad. The sphere of application becomes broader and the scale of problems being solved becomes larger. In China today, there are groups of experienced researchers working in this field, in particular at the Institute of System Sciences of the Chinese Academy of Sciences, the Jiangxi University, the Jilin Technical University, the People's University, and the Shanghai Jiao Tong University. In this paper, we will give a sketch of the results obtained by our colleagues since 1979. The paper consists of seven parts.