Elitist Non-dominated Sorting Genetic Algorithm Research Articles

A multi-objective approach for the trajectory planning of a 7-DOF serial-parallel hybrid humanoid arm

Humanoid robots are an elusive long-term goal in robotics, and a humanoid arm is an essential actuator. Inspired by the motion characteristics of the human arm, a seven-degree-of-freedom (DOF) serial-parallel hybrid humanoid robotic arm is proposed. With the mechanism as an example, a solution of multi-objective trajectory planning is addressed using an indirect method to execute a point-to-point mission with faster time, lower energy and higher stability. First, to avoid abrupt changes of the joint angle, the path with a better joint-rate distribution is gained based on normalized Jacobian condition number. On the basis of this path, a multi-optimization model considering travelling time, energy consumption and torque fluctuation is formulated to plan the trajectory interpolated by quintic B-splines. The elitist non-dominated sorting genetic algorithm (NSGA-II) is used to address the model, and the optimal solution is determined by defining the weight value of the objective. Then, the complete dynamic model is obtained by combining equilibrium and deformation coordination equations. Finally, compared with another important trajectory planning methodology, the feasibility and effectiveness of the proposed methodology are validated through numerical simulations.

Energy management and emission control for range extended electric vehicles

Range extended electric vehicles (REEVs), transition vehicles between combustion engine vehicles and electric vehicles, have been widely used due to their advantages of low fuel consumption and low emissions. By optimizing power management and after-treatment system control strategies, it is possible to achieve lower fuel consumption and emissions. This study proposes an integrated control method based on optimization strategies for the auxiliary power unit (APU) on/off system and energy management optimization for extended hybrid electric vehicles. The Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) optimization method is used to determine the control parameters. In addition, considering the frequent start and stop characteristics of the APU caused by the optimization strategy, closed-loop control of the urea injection is established to solve the ammonia leakage problem. Due to their high computational efficiency, the proposed energy management and urea injection algorithms can be easily implemented in real time. The actual operating emissions of engines under the different strategies are tested on a semi-physical simulation platform, focusing on the analysis and comparison of NOx emissions. The conclusion has certain reference significance for vehicle production.

Modeling and suppression of unbalanced radial force for in-wheel motor driving system

Considering the negative vertical dynamics effect of switched reluctance motor on an in-wheel motor driving system, this article presents a modeling and suppression method for unbalanced radial force of the in-wheel motor driving system. To tease out the coupling relationship within the in-wheel motor driving system, this investigation, respectively, explores the principle of unbalanced radial force and the coupling relationship between rotor eccentricity and road excitation based on the suspension response model with unbalanced radial force under road excitation. The switched reluctance motor nonlinear analytical model was fitted by the Fourier series, and its radial electromagnetic force was modeled and analyzed by the Maxwell stress tensor method. To mitigate the influence of radial electromagnetic force fluctuation and unbalanced radial force amplitude value under eccentricity condition on the in-wheel motor driving system, the elitist non-dominated sorting genetic algorithm was adopted to improve radial electromagnetic force fluctuation and unbalanced radial force amplitude value of the switched reluctance motor. The simulation results show that the proposed optimization method can suppress the radial electromagnetic force fluctuation and unbalanced radial force amplitude value, and the negative effect of vertical dynamics of the in-wheel motor driving system is conspicuously mitigated.

Optimized resource-constrained method for project schedule compression

PurposeThis paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.Design/methodology/approachThe proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.FindingsThe developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.Originality/valueThe novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

Nano-catalytic heterogeneous reactive distillation for algal biodiesel production: Multi-objective optimization and heat integration

This work proposes a novel reactive distillation (RD) column for the production of algal biodiesel by the use of a heterogeneous nano-catalyst, Ca(OCH3)2. The elitist non-dominated sorting genetic algorithm (NSGA-II) is employed to optimize the design of this RD column to maximize biodiesel purity while minimizing total annual cost and CO2 emissions. The optimal point is recommended by a Pareto ranking method, namely, technique for order of preference by similarity to ideal solution (TOPSIS) with entropy information for the weighting of objectives. To improve biodiesel purity, a decanter is proposed to couple with the RD column at the bottom. Subsequently, performance of the optimal RD column is further improved by retrofitting it with the vapor recompression arrangement. In this proposed intensified configuration, a reactive stage is thermally integrated with the reboiler to constitute a side vapor recompressed RD (SVRRD) column. Finally, a comparative study is performed to quantify the performance improvement of the proposed SVRRD column over the conventional RD column. It is found that there is a 34.6% reduction in CO2 emissions with a payback period of 4.54 years for the vapor recompressor. Hence, the proposed SVRRD is attractive with better performance in terms of energetic, environmental and economic perspectives.

Different nanofluids as coolant in heat exchanger network: Thermoeconomic modeling and multi-objective optimization

Modeling and optimization of a multi tube heat exchanger (MTHE) network considering the effects of different nanoparticles on the tube side are carried out using Fast and elitist non-dominated sorting genetic algorithm. After thermal modeling in [Formula: see text] method, optimization is performed by increasing the effectiveness and decreasing total annual cost as two objective functions using eight design parameters such as number of MTHE and particles volumetric concentration. In addition, optimization is performed at three various cold mass flow rates and different nanoparticles including Al2O3, CuO and ZrO2 and results are compared with the base fluid (water). For the reliability of the present code, the modeling results are validated with the results obtained from both the numerical and experimental model. The results show that the optimal Pareto front is improved in nanoparticles case, and the rate of improvement in CuO nanoparticles case, especially in higher effectiveness and lower cold mass flow rate is more significant compared with the other studied cases. In addition, because of improvement in the thermal performance of MTHE network with nanoparticles, the heat transfer surface area and consequently the total volume of MTHE network for the fixed values of effectiveness are noticeably reduced. Finally, the effects of design parameters versus effectiveness are demonstrated and discussed.

Optimization of parameters for air dehumidification systems including multilayer fixed-bed binder-free desiccant dehumidifier

In air dehumidification systems, the optimization of operating conditions is as important as the improvement of the dehumidifier itself under varying loads. Therefore, a comprehensive parameter analysis and optimization study of a novel air dehumidification system, which consists of a multilayer fixed-bed binder-free desiccant dehumidifier and a water cooling and heating device, is proposed herein. First, a mathematical model to predict the transient heat and mass transfer in the air dehumidification system is established and validated by comparing with the experimental results. Second, the cyclic performance of the system is analyzed and the influences of each parameter, including operating, structural, and adsorption and transport parameters, are analyzed. Third, the optimization of operating parameters is conducted based on the combination of a backpropagation (BP) neural network and genetic algorithms (GAs). The operating parameters are optimized by maximizing the energy efficiency and dehumidification performance and a trade-off relation is identified between them. For three typical load conditions, it is successfully demonstrated that the optimum energy efficiency and dehumidification performance can be determined from the Pareto front obtained by an elitist non-dominated sorting GA (NSGA Ⅱ) within the requirement specifications and the optimum operating parameters can also be determined by the combination of a BP neural network and a NSGA Ⅱ.

Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation.

Using following heroes operation in multi-objective differential evolution for fast convergence

In this study, a new hybrid variant of multi-objective differential evolution is developed to improve the extent and the speed of convergence by combining the ability of DE/rand/1 strategy (NSDE1) to retain only better solutions in the selection operation with the ability of Following Heroes operation from adaptive social evolution algorithm to rapidly propagate the effect of the best solutions to the entire population. Such an algorithm is quite useful for fast convergence, particularly in solving computationally intensive multi-objective optimization problems for which it is highly desirable to obtain the better quality optimal solutions in a limited number of function calculations. The performance of the algorithm is first analyzed on thirty multi-objective test problems from ZDT, DTLZ, and WFG test suites for a limited number of function calculations, and the results are compared with four well-established multi-objective optimization algorithms: real-coded elitist non-dominated sorting genetic algorithm (RNSGA-II), non-dominated sorting particle swarm optimization (NSPSO), adaptive social evolution (ASE) and non-dominated sorting differential evolution with DE/rand/1 strategy (NSDE1) and found to be superior. The analysis is further extended to model-based algorithms, Pareto efficient global optimization (ParEGO), kriging for expensive evaluation Pareto (KEEP), and surrogate optimization of computationally expensive multi-objective problems (SOCEMO) which are commonly used for computationally intensive problems. These involve an additional computational cost of evolving the surrogate models. The performance of the proposed algorithm is found to be better than these, though, it has a relatively simple algorithmic structure and does not involve any additional computational cost. The algorithm is then evaluated on computationally intensive multi-objective optimization of bulk polymerization of methyl methacrylate. The numerical results obtained for this problem show better convergence for a limited number of function calculations.

A bi-objective simulation-based optimization algorithm for redundancy allocation problem in series-parallel systems

The present study proposes a bi-objective simulation-based optimization model applicable to the redundancy allocation problem (RAP) with heterogeneous components for the objective functions of system reliability maximization and system cost minimization. Proposed RAP identifies the optimal component types, the redundancy level, and the redundancy strategy, comprising active, cold-standby, mixed, or K-mixed configurations, with imperfect switching. Based on the stochastic nature and NP-hard complexity of the problem, except for the active redundancy strategy, there is no analytical closed-form method for accurately assessing system reliability. Hence, earlier studies carried out system reliability optimization by single-stage stochastic techniques that estimate the lower Lagrangian function bound. This limitation adds to the design cost and hinders greater system reliability. Generally, one cannot analytically evaluate system reliability. The present study employs simulation sampling in order to make efficient and unbiased reliability estimates. 4Dscript interpreting programming is utilized to design the computerized simulation model. Since RAP has a combinatorial nature, the present study exploits the controlled elitist non-dominated sorting genetic algorithm (NSGA-II) to obtain Pareto-optimal fronts with properly-distributed optimal points. Various benchmark solutions of the literature are investigated to validate the developed model and evaluate the proposed NSGA-II method efficiency. The findings revealed satisfactory performance in system reliability enhancement and total system cost reduction compared to the earlier methods.

Design optimization of triple friction pendulums for high-rise buildings considering both seismic and wind loads

Triple friction pendulum isolation bearings are typically designed with only earthquakes in mind, or perhaps with a large enough initial friction coefficient to limit displacement under wind loads. However, for potential application in high-rise buildings, wind demand is of greater concern, and the increased flexibility of the structure can lead to wind-induced floor accelerations that exceed the serviceability limit state. To determine the optimum parameters of triple friction pendulum bearings that minimize the structural response under both wind and seismic loading in high-rise buildings, an optimization methodology (fast elitist non-dominated sorting genetic algorithm) is used. Cost functions are proposed that penalize the story drift and floor acceleration under wind and all earthquake hazard level as well as isolation displacement under large earthquake events. First, a genetic algorithm (GA) is adopted to find the optimal parameters of the bearings designed separately for wind and earthquake excitations. However, the triple friction pendulum bearing (TFPB) designed optimally for earthquake excitation provides less than optimal wind-resistant performance and vice-versa. Afterwards, a multi-objective optimization is used which allows for a tradeoff between the seismic isolation and wind-resistant performance. The design parameters of TFPB can then be selected to achieve the best seismic isolation performance while ensuring that the performance under wind-loading meets requirements.

Solving a bi-objective unmanned aircraft system location-allocation problem

In this paper we introduce a bi-objective location-allocation problem for Unmanned Aircraft Systems (UASs) operating in a hostile environment. The objective is to find the locations to deploy UASs and assign Unmanned Aerial Vehicles to regions for surveillance. One of the objectives is to maximize search effectiveness, while the second is the minimization of the threats posed to the UASs. These two objectives are in conflict, because they are affected differently by the proximity between the UAS locations and the target regions. First, we have formulated this problem as a mixed integer nonlinear program. Next, we have developed its linearization which can be solved by a commercial optimizer for small-scale problem instances. To solve large-scale problems, we have adopted a well-known metaheuristic for multi-objective problems, namely the elitist non-dominated sorting genetic algorithm. We have also developed a hybrid approach, which has proven to be more effective than each approach alone.

Optimal Planning of Active Distribution Network Based on Soft Open Point and Shunt Capacitors

As the penetration of distributed wind power in the distribution network continues to increase, the uncertainty of its output has a serious impact on the stable operation of the distribution network. It is difficult to meet the voltage regulation requirements when the wind power fluctuates frequently only by relying on shunt capacitors. Therefore, a coordinated optimization planning method based on soft open point (SOP) and shunt capacitors is proposed. Firstly, the bidirectional generative adversarial network (BIGAN) is used to characterize the uncertainty of wind power output and generate typical scenarios of wind power output. Secondly, a multi-objective optimization planning model of SOP and shunt capacitors is proposed based on the scene analysis method; Then, a solution strategy based on the improved elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed. Finally, the proposed planning model and solution are verified and analyzed in the improved IEEE 33-bus system.

Exergoeconomic and exergoenvironmental analyses and optimization of a new low-CO2 emission energy system based on gasification-solid oxide fuel cell to produce power and freshwater using various fuels

This study proposes a novel biomass gasification-based solid oxide fuel cell (SOFC) integrated with a gas turbine, a thermoelectric generator, a multi-stage flash desalination with brine recirculation unit (MSF-BR), and a CO 2 capture unit for power and freshwater production. Exergoeconomic and exergoenvironmental analyses of using four biomass fuels, namely, municipal solid waste (MSW), wood, paper, and sawdust on the newly proposed system are carried out. Subsequently, the thermodynamic performance, cost, and environmental impact (EI) indicators of the system are investigated under the varying substantial operating parameters for each selected biomass fuel. Then, the system performance is optimized based on the thermodynamic efficiencies, product cost, and EI of the overall system by applying the fast and elitist non-dominated sorting genetic algorithm (NSGA-II). The results exhibit that the highest produced freshwater and output power are achieved by 46.05 m 3 /day, and 228.7 kW for wood and sawdust, respectively, and the lowest levelized CO 2 emission is achieved to be 0.138 ton/MWh for sawdust. Moreover, better total product cost and EI rate of the system are computed to be 2.106 $/h and 14.97 Pts/h, respectively, for MSW. Therefore, MSW is recognized as the best cost and environment-friendly fuel.

Multi-level fleet size optimization for containers handling using double-cycling strategy

Every few years, larger containerized vessels are introduced to the market to accommodate the increase in global trade. Although increasing the capacity of vessels results in maximizing the amount of imported and exported goods per voyage, yet it is accompanied with new challenges to terminal planners. One of the primary challenges is minimizing the vessel turnaround time with the least possible cost. In this context, this paper presents the development of a multi-level optimization model using the elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal or near-optimal fleet size combination of the different container handling equipment used in the terminal. The model aims to minimize two conflicting objective functions, namely, vessel turnaround time and total handling cost. Furthermore, the model considers a double-cycling strategy for the container handling process to achieve increased productivity and eventually more reduction in the vessel turnaround time. The model was implemented on a real-life case study to demonstrate its efficiency and the benefit of employing the double-cycling strategy compared with the traditional single-cycling strategy. The results demonstrated the efficiency of employing the double-cycling strategy by providing a reduction of above 20% in both the vessel turnaround time and the total handling cost and an increase of above 25% in the productivity when compared to the traditional single-cycling strategy.

Two-objective robust job-shop scheduling with two problem-specific neighborhood structures

This paper discusses a two-objective robust job-shop scheduling problem with uncertain processing times described by discrete scenarios. The two objectives are to minimize the mean makespan and the worst-scenario makespan across all the scenarios. Two hybrid algorithms are developed by combining the elitist nondominated sorting genetic algorithm (NSGA-II) and tabu search (TS) operators. Two kinds of neighborhood structures are constructed based on the problem-specific knowledge for discrete scenarios and used in local TS operators, which are performed for each individual to improve offspring population. The hybrid algorithms with two specialized neighborhood structures are compared with each other, with the state-of-the-art existing algorithms for solving the discussed problem. The computational results verify the original intentions of designation of two neighborhood structures as well as the advantages of the developed hybrid algorithms for the proposed two-objective problem.

Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm

The introduction of electric vehicles (EVs) will have an important impact on global power systems, in particular on distribution networks. Several approaches can be used to schedule the charge and discharge of EVs in coordination with the other distributed energy resources connected on the network operated by the distribution system operator (DSO). The aggregators, as virtual power plants (VPPs), can help the system operator in the management of these distributed resources taking into account the network characteristics. In the present work, an innovative hybrid methodology using deterministic and the elitist nondominated sorting genetic algorithm (NSGA-II) for the EV scheduling problem is proposed. The main goal is to test this method with two conflicting functions (cost and greenhouse gas (GHG) emissions minimization) and performing a comparison with a deterministic approach. The proposed method shows clear advantages in relation to the deterministic method, namely concerning the execution time (takes only 2% of the time) without impacting substantially the obtained results in both objectives (less than 5%).

Finance-based scheduling multi-objective optimization: Benchmarking of evolutionary algorithms

Project scheduling and financing should be adequately integrated during the planning phase to avoid probable cost overruns and delays. Many studies addressed the achievement of integration between project financing and scheduling using multi-objective optimization in particular. However, up to the knowledge of the authors, there is no research conducted to evaluate and assess the performance of the multi-objective optimization techniques employed in this domain. Thus, the current study developed a finance-based scheduling multi-objective optimization model for multiple projects using the elitist non-dominated sorting genetic algorithm (NSGA-II). Further, the obtained results were compared with the results obtained by solving the same problem in another study from the literature using the multi-objective optimization technique of strength Pareto evolutionary algorithm (SPEA). Benchmarking was conducted based on the quality of the obtained solutions and performance. The results indicated that the NSGA-II outperformed SPEA in most aspects with achieved improvements range from 1.7% to 98.2%.

Toward a sustainable system of wastewater treatment plants in Chile: a multi-objective optimization approach

Over the past fifty years the population of the world has doubled, while resources as water have become increasingly scarce. In particular, water consumption has far exceeded the available of this resource in some regions of the world. In order to address the above problem, the possibility of reusing grey water by installing wastewater treatment plants could be a suitable alternative for several developing countries. This paper seeks to find the best configuration for these facilities in Chile by considering the economic and environmental aspects conjoint to the social dimension. The problem is modeled as a multi-objective optimization including: minimizing costs, minimizing environmental impact, maximizing phosphorus extraction from wastewater and maximizing the number of workers to be required with the goal of analyzing the sustainability of the system. To find the Pareto frontiers of multi-objective problem, a resolution framework based on an adaptation of elitist non-dominated sorting genetic algorithm (NSGA-II) is provided for the problem. From the obtained results, the non-dominated solutions and a compromise solution are computed, reporting configuration alternatives that integrate the three sustainability dimensions, the economic, the environmental and the social as objectives for the design for a sustainable system of wastewater treatment plants.

Multi-objective optimization for attitude maneuver of liquid-filled flexible spacecraft based on improved hierarchical optimization algorithm

To solve the multi-objective optimization problem for attitude maneuver of liquid-filled flexible spacecraft, the authors propose a novel algorithm based on an improved hierarchical structure. This improved hierarchical optimization algorithm (IHOA) is composed of elitist non-dominated sorting genetic algorithm (NSGA-II) and bare-bones multi-objective particle swarm optimization based on r-dominance relation (r-BBMOPSO). Bottom layer algorithm NSGA-II provides elitist individuals for top layer algorithm. Top layer algorithm r-BBMOPSO employs r-domination instead of Pareto domination to strengthen selection pressure and guide the search toward the decision maker’s preference. The modification of hierarchical structure has reduced the influence of randomness and accelerated convergence speed while preserving the operating efficiency of the algorithm. The feasibility and effectiveness of IHOA are demonstrated by bench-mark test problems. Also, the simulation results proved that the proposed optimization algorithm IHOA has the capacity to stably generate solutions which are satisfied the decision maker’s demand in the case of a single run. Especially, based on the optimized parameters, the spacecraft can perform well in attitude maneuver tasks.