A computer‐aided design tool dedicated to isolated DC‐DC converters based on multiobjective optimization using genetic algorithms

Thermodynamic modeling and multi-objective evolutionary-based optimization of a new multigeneration energy system

Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques

Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm

Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm

An adaptation, inspired by the concept of jumping genes in biology, is developed for the binary-coded elitist nondominated sorting genetic algorithm (NSGA-II). This helps in obtaining global-optimal solutions faster, particularly for problems involving networks. This is because the optimal values of some decision variables in such problems may be 0 or 1, e.g., some streams may be nonexistent in the optimal configuration. It is difficult to generate such chromosomes in the binary-coded NSGA-II (or the unmodified version of the real coded NSGA-II) using the three conventional operations of reproduction, crossover, and mutation. The algorithm developed is used to solve a few sample simple problems involving froth flotation circuits, which represent an important problem in mineral beneficiation. A two-species, two-cell flotation circuit is studied. Both single-objective as well as multi-objective optimizations are performed. The two important objectives used are as follows: (i) the maximization of the recovery of the concentrated ore and (ii) the maximization of the valuable-mineral content (grade) in the concentrated ore. A constraint of a fixed total flotation cell volume is also used. Because these objectives are conflicting, Pareto sets of nondominated solutions are obtained. The algorithm also can be used for the optimization of other networks.

In this paper, separation of a ternary mixture based on simulated moving bed (SMB) technology is studied. The conventional four-zone SMB system for binary separation was modified into different configurations to facilitate simultaneous collection of all three components. The performances of these systems were compared at optimal conditions based on multiobjective optimization for an industrial-scale separation of C8 aromatics. Simultaneous recovery of p-xylene, ethylbenzene, and mixtures of o- and m-xylene with the highest possible purities are considered. The effect of a reflux stream addition containing one of the components is also investigated. It was observed that, as the separation becomes more difficult, the five-zone SMB performs better than the modified four-zone SMB for ternary separation, while at the same time consuming a lesser amount of desorbent. Efficacies in overall performances of the different modified SMB configurations are discussed. Rigorous multiobjective optimization is performed using a nontraditional optimization technique based on an adaptation of a genetic algorithm, elitist nondominated sorting genetic algorithm with jumping genes.

Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA)

Simultaneous multi-objective optimization of a PHEV power management system and component sizing in real world traffic condition

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

The parameter estimation tuning is one of the procedures of system identification. One of the identification problems is to find an optimal model structure especially for representing the complex multivariable dynamic systems. Therefore, this problem needs to be solved by using multi-objective optimization with two objective functions namely minimum predictive error and model complexity. In this paper, the tuning of control parameters is studied before comparing multi-objective evolutionary algorithms. The framework is based on the system identification problems. In parameter tuning, two main qualitative parameters of evolutionary algorithms are focused on crossover rate and mutation rate. The multi-objective evolutionary algorithms used for this study are an elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective optimization using differential evolution (MOODE). The different parameter settings are compared in order to obtain good parameter setting for NSGA-II and MOODE. Then, the performance for both algorithms is also compared using Pareto front. The plotted graphs of two objective functions show the non-dominated fronts achieved by the algorithms. The results proved that MOODE shows an advantage in solving system identification problems in a multivariable nonlinear dynamic system.

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

Multiobjective optimization (MOO) has been successfully used to improve the process design and operation, by finding trade-offs among conflicting objectives such as energy, capital cost, and profit. In this work, the cumene process design is modified to decrease the raw materials and product losses and to facilitate better energy integration. Here, two slightly different cumene process designs are presented and evaluated. One process design uses a column to vent off the undesired chemicals, whereas the other uses two flash tanks. Additionally, vapor recompression is applied in both the designs to recover energy. Then, MOO of both modified process designs is carried out to examine two trade-offs: total capital cost (TCC) versus material loss and TCC versus utility cost. For this, an Excel-based MOO program is used; it is based on the elitist nondominated sorting genetic algorithm. The cumene process design with column is found to be superior for the first trade-off, whereas the design with two flash tanks is better for the other trade-off. Further, both the designs are compared based on their cumene production capacities; column design is found to be overall superior. Finally, energy requirements of the developed cumene process designs are compared with those reported in recent studies.

Energy storage technology is crucial for promoting the replacement of traditional energy with renewable energy and regulating the energy supply–demand relationship. This paper investigates a triplex-tube thermal energy unit storage to solve the intermediate heat storage and heat transfer problem of hot water supply and demand in clean heating systems. A multi-objective optimization method based on the elitist non-dominated sorting genetic algorithm (NSGA-II) was utilized to optimize the geometric dimensions (inner tube radius r1, casing tube radius r2, and outer tube radius r3), focusing on heat transfer efficiency (ε), heat storage rate (Pt), and mass (M). On this basis, the influence of the optimization variables was analyzed. The optimized configuration (r1=0.014 m, r2=0.041 m, and r3=0.052 m) was integrated into a modular design, achieving a 2.12% improvement in heat transfer efficiency and a 73.23% increase in heat storage rate. Experimental results revealed that higher heat transfer fluid (HTF) temperatures significantly reduce heat storage time, while HTF flow rate has a minimal impact. Increasing the heat release temperature extends the phase change material (PCM) heat release duration, with the flow rate showing negligible effects. The system’s thermal supply capacity is susceptible to heat release temperature.

In this paper, we present a weight-based multi-objective immune genetic algorithm (WBMOIGA). Compared to other weight-based multiobjective genetic algorithms, the proposed algorithm shows the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function, and (2) a local search procedure is utilized to improve the quality of the population, and an immune operator is adopted to increase the diversity of the population, and (3) specifically, a new mate selection operator, called tournament selection algorithm with similar individuals (TSASI), and a new environmental selection operator, named truncation algorithm with similar individuals (TASI), are presented. Simulation results on six standard test problems (ZDT1,ZDT2,ZDT3,ZDT6,SCH2,and FON) show WBMOIGA can find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist non-dominated sorting genetic algorithm (NSGA-II) and the random weight genetic algorithm (RWGA). Moreover, when applied to parallel machine scheduling, WBMOIGA also demonstrates better performance than NSGA-II and RWGA.