Multi-Objective Particle Swarm Optimization: An Introduction

Vipin Kumar,Sonajharia Minz 한국산학기술학회 2014 SmartCR Vol.4 No.5

In the real world, reconciling a choice between multiple conflicting objectives is a common problem. Solutions to a multi-objective problem are those that have the best possible negotiation given the objectives. An evolutionary algorithm called Particle swarm optimization is used to find a solution from the solution space. It is a population-based optimization technique that is effective, efficient, and easy to implement. Changes in the particle swarm optimization technique are required in order to get solutions to a multi-objective optimization problem. Therefore, this paper provides the proper concept of particle swarm optimization and the multi-objective optimization problem in order to build a basic background with which to conduct multi-objective particle swarm optimization. Then, we discuss multi-objective particle swarm optimization concepts. Multi-objective particle swarm optimization techniques and some of the most important future research directions are also included.

Approximation Models for Multi-Objective Optimization

Yeun, Y.S.,Yang, Y.S.,Jang, B.S.,Ruy, W.S. 대진대학교 생산기술연구소 2000 생산기술연구소 논문집 Vol.3 No.-

In engineering problems, computationally intensive high-fidelity models or expensive computer simulations hinder the use of standard optimization techniques because they should be invoked repeatedly during optimization, despite the alarming growth of computer capability. Therefore, these expensive analyses are often replaced with approximation models that can be evaluated nearly free. However, due to their limited accuracy, it is practically impossible to exactly find an actual optimum(or a set of actual noninferior solutions) of the original single(or multi-objective) optimization problem. Significant efforts have been made to overcome this problem, The model management framework is one of such endeavours. The approximation models are sequentially updated during the iterative optimization process in such a way that their capability to accurately model original functions especially in the region of our interests can be improved. The models are modified using one or several sample points generated by making good use of the predictive ability of the approximation models. However, theses approaches have been restricted to a single objective optimization problem. It seems that there is no reported management framework that can handle a multi-objective optimization problem. This paper will suggest strategies that can successfully treat not only a single objective but also multiple objectives by extending the concept of sequentially managing approximation models and combining this extended concept with the Genetic Algorithm which can treat multiple objective s(MOGA). Consequently, the number of exact analysis required to converge an actual optimum or to generate a sufficiently accurate Pareto set can be reduced considerably. Especially, the approach for multiple objectives will lead to the surprising reduction in the number. We will confirm these effects through several illustrating examples. Key words : optimization, approximation model, model management framework, multi-objective

Optimization Design for Dynamic Characters of Electromagnetic Apparatus Based on Niche Sorting Multi-objective Particle Swarm Algorithm

Le Xu,Jiaxin You,Haidan Yu,Huimin Liang 한국자기학회 2016 Journal of Magnetics Vol.21 No.4

The electromagnetic apparatus plays an important role in high power electrical systems. It is of great importance to provide an effective approach for the optimization of the high power electromagnetic apparatus. However, premature convergence and few Pareto solution set of the optimization for electromagnetic apparatus always happen. This paper proposed a modified multi-objective particle swarm optimization algorithm based on the niche sorting strategy. Applying to the modified algorithm, this paper guarantee the better Pareto optimal front with an enhanced distribution. Aiming at shortcomings in the closing bounce and slow breaking velocity of electromagnetic apparatus, the multi-objective optimization model was established on the basis of the traditional optimization. Besides, by means of the improved multi-objective particle swarm optimization algorithm, this paper processed the model and obtained a series of optimized parameters (decision variables). Compared with other different classical algorithms, the modified algorithm has a satisfactory performance in the multi-objective optimization problems in the electromagnetic apparatus.

Multi-objective optimization using evolutionary algorithm for axial flow pump impeller

H.S. Park,Fu-qing Miao (사)한국CDE학회 2013 한국CAD/CAM학회 국제학술발표 논문집 Vol.2010 No.8

Evolutionary algorithms (EA) are a unique and attractive approach to the real world multiobjective optimization design such as a axial flow pump impeller optimization design problem. In this paper, multi-objective evolutionary algorithm to the axial flow pump impeller optimization design is presented. In axial flow pump design process, in order to get high performance pump, designers usually try to increase the efficiency (η) and decrease the required NPSH (NPSHr) simultaneously. In this paper, multi-objective optimization of axial flow pump based on modified evolutionary algorithm Particle Swarm Optimization (MPSO) is performed. At first, the NPSHr and η in a set of axial flow pump are numerically investigated using commercial software ANSYS with the design variables concerning hub angle βh, chord angle βc, cascade solidity of chord σc, maximum thickness of blade H. And then, using the Group Method of Data Handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to design variables are obtained. Finally, multi objective optimization based on modified Particle Swarm Optimization (MPSO) approach is used for Pareto based optimization. The result shows that an optimal solution of the axial flow pump impeller was obtained: NPSHr was decreased by 11.68% and efficiency was increased by 4.24% simultaneously. It means this optimization is feasible.

Mono and multi-objective optimization techniques applied to a large range of industrial test casesusing Metamodel assisted Evolutionary Algorithms

Lionel FOURMENT,Richard DUCLOUX,Stephane MARIE,Mohsen EJDAY,Dominique MONNEREAU,Thomas MASSE,Pierre MONTMITONNET 한국소성가공학회 2010 기타자료 Vol.2010 No.6

The use of material processing numerical simulation allows a strategy of trial and error to improve virtual processes without incurring material costs or interrupting production and therefore save a lot of money, but it requires user time to analyze the results, adjust the operating conditions and restart the simulation. Automatic optimization is the perfect complement to simulation. Evolutionary Algorithm coupled with metamodelling makes it possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. Ten industrial partners have been selected to cover the different area of the mechanical forging industry and provide different examples of the forming simulation tools. It aims to demonstrate that it is possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. The large computational time is handled by a metamodel approach. It allows interpolating the objective function on the entire parameter space by only knowing the exact function values at a reduced number of “master points”. Two algorithms are used: an evolution strategy combined with a Kriging metamodel and a genetic algorithm combined with a Meshless Finite Difference Method. The later approach is extended to multi-objective optimization. The set of solutions, which corresponds to the best possible compromises between the different objectives, is then computed in the same way. The population based approach allows using the parallel capabilities of the utilized computer with a high efficiency. An optimization module, fully embedded within the Forge2009 IHM, makes possible to cover all the defined examples, and the use of new multi-core hardware to compute several simulations at the same time reduces the needed time dramatically. The presented examples demonstrate the method versatility. They include billet shape optimization of a common rail, the cogging of a bar and a wire drawing problem.

Asymptotically Optimal Scenario-based Multi-objective Optimization for Distributed Generation Allocation and Sizing in Distribution Systems

Lizhen Wu,Xusheng Yang,Hu Zhou,Xiaohong Hao 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.4

Suitable location and optimal sizing are impact on voltage stability margin of the distributed system. It is important to accurately simulate the random output active power of Distributed Generation (DG). In order to model uncertainties of intermittent distributed generation and load, this paper proposes a multi-scenario tree model of wind-photovoltaic-load using multiple scenarios technique based on the Wasserstein distance metrics, which generates asymptotically optimal scenario. And in this paper, a multi-objective optimizes control model with scenario tree is presented, which including objectives that are the total active power losses and the voltage deviations of the bus. Moreover, a new hybrid Honey Bee Mating Optimization and Particle Swarm Optimization (HBMO-PSO) algorithm is proposed to solved the problems. In the HBMO-PSO algorithm, the mating process is corrected, which the PSO algorithm is combined with the HBMO algorithm to improve the performance of HBMO. Finally, a typical IEEE 33-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method. Simulation results illustrate the correctness and adaptability of the proposed model and the improved algorithm.

Multi-Objective Optimization Model of Electricity Behavior Considering the Combination of Household Appliance Correlation and Comfort

Zhaoyang Qu,Nan Qu,Yaowei Liu,Xiangai Yin,Chong Qu,Wanxin Wang,Jing Han 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.5

With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer’s load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

Multi-objective Morphology Optimization of Free-form Cable-braced Grid Shells

Ruo-qiang Feng,linlin Zhang,Jin-ming Ge 한국강구조학회 2015 International Journal of Steel Structures Vol.15 No.3

This paper examines the multi-objective morphology optimization of free-form cable-braced grid shells. First, according to the shape forming method for grid shells, shape optimization can be realized by adjusting the generatrix and directrix rather than optimizing the whole surface. Second, the multi-objective shape optimization of free-form cable-braced grid shells is conducted. According to different practical requirements, the mechanical and geometric indexes, mechanical and economic indexes, or different mechanical indexes are used as multiple optimization objectives. Four main conclusions can be drawn from this study. First, with the above shape optimization method, the optimized surface does not change significantly; therefore, this method is useful in the shape optimization of grid shells with given initial surfaces. Second, among the static mechanical performance indexes, the mechanical behavior of the cable-braced grid shell is better with strain energy as the optimization objective. Third, with the weight of steel tubes and strain energy as the optimization objectives, the result of the multi-objective optimization that combines section optimization with shape optimization is favorable and practical in engineering applications. Fourth, when strain energy and the variance of the tube lengths are used as multiple optimization objectives, the structural mechanical behavior is not sensitive to the weight factor of variance of the tube lengths.

다중목적 최적화 알고리즘 기반 복합커뮤니티공간 디자인 연구

주형일 ( Ju¸ Hyungil ),이우형 ( Lee¸ Woohyoung ) 한국공간디자인학회 2020 한국공간디자인학회논문집 Vol.15 No.6

(Background and Purpose) Recently, a number of complex projects are underway to expand green spaces and living SOC by utilizing idle lands in the cities. The purpose of these projects is to secure environmental performance that is the most essential requirement for a community complex located in the city center. Therefore, this study focuses on the importance of the environmentally optimal relationship between the exterior spaces, where the influence of the adjacent context is reflected, and the buildings and proposes a community complex design as a counterplan by optimization. To this end, an exemplary site with specific conditions is selected and a multi-objective genetic algorithm that simultaneously computes multiple conditions linked with environmental data is applied to derive an optimal site plan and an architectural design. (Method) As an exemplary site, an idle land that remains as a physical, socio-economic boundary in the communities due to the demolition of old facilities, is selected, and an overall plan for a community complex is drafted based on the optimization process. Rhino, Grasshopper, parametric design tools, Ladybug, an add-on for environmental analysis, and Octopus, an add-on for multi-objective optimization, are used. First, the site plan and architectural form optimized to achieve the optimal environmental conditions for solar radiation within the site are derived. Then, the optimal configuration for the external shading system is derived. The location of the buildings is determined based on the influence of the adjacent context and the relationship between each building; the architectural form is determined based on the orientation of the building linked to the solar radiation and the exterior mass. the exterior shading system is determined based on the optimal shielding rate by the size and spacing of each pin depending on the horizontal and vertical shading systems. (Results) The design included not only the site plan, form and exterior design, but urban and architectural proposals that respond to problems of the communities. First, to resolve the problems which have been cut off by the site, the social integration of the communities were reinforced by providing facilities and functions that would meet the needs of the communities. From the architectural perspective, the environmental performance of the urban park including facilities was optimized by genetic algorithm optimization. From a technical perspective, a multi-objective optimization process that goes beyond the limitations of the existing single-objective optimization. (Conclusions) Multi-objective optimization that utilizes environmental data was introduced into the design process to propose the possibility of a new performance-oriented design method based on an environmental perspective. Furthermore, this study is of great significance in that it proposed a pioneering perspective for practical integration of new technology and architectural design.

Congestion Management in Deregulated Power System by Optimal Choice and Allocation of FACTS Controllers Using Multi-Objective Genetic Algorithm

S. Surender Reddy,Matam Sailaja Kumari,Maheswarapu Sydulu 대한전기학회 2009 Journal of Electrical Engineering & Technology Vol.4 No.4

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.