Multi-objective Particle Swarm Optimization Method Research Articles

Multi Objective Particle Swarm Optimization (MOPSO) for Size and Shape Optimization of 2D Truss Structures

1024x768 This paper covers optimization techniques for trusses to find the most efficient cross sections and configuration of joints. The improvement will be achieved by applying changes in one or both of these parameters. Objective functions for optimization are weight and the deflection of the truss’s joints. For optimization of both weight and deflection the MOPSO method is used. This is a powerful method that enables the optimization of huge trusses. The former methods for optimization of the shape or size of the trusses, was done separately and as a single objective while this paper covers a new way via multi objective methods. For proofing the ability of the represented method in this paper, some standard examples are compared. The comparison of the results shows good accuracy and desirable verity of pareto front. Normal 0 false false false EN-US X-NONE AR-SA Normal 0 false false false EN-US X-NONE AR-SA

Dynamical bending analysis and optimization design for functionally graded thickness (FGT) tube

As a relatively new component with a higher efficiency of material utilization, functionally graded thickness (FGT) structure with desired varying wall thickness has been becoming more and more attractive. In order to sufficiently understand the crashworthiness of FGT under lateral impact, firstly, the finite element (FE) models of thin-walled columns with uniform thickness (UT) and FGT under lateral loading are established and validated by experimental results. It is exhibited that the FE simulations are in good agreement with experimental tests. Then, the crashworthiness of UT tube and the corresponding FGT tube is compared, and the results reveal that the FGT tube can absorb more energy but generate larger force than UT tube under the same weight. Further, parametric analyses show the gradient exponent, wall thickness range, the tube diameter and yielding stress have significant effects on the crashworthiness of FGT tubes. Finally, a multiobjective particle swarm optimization (MOPSO) method is used to optimally seek for those design parameters, where surrogate modeling methods are adopted to formulate the specific energy absorption (SEA) and peak crushing force functions. The results yielded from the optimization demonstrate that the FGT tube is superior to its uniform thickness counterpart in overall crashing behavior under lateral impact. Therefore, FGT tubes are recommended as potential absorbers of crashing energy under lateral loading.

Multi-Objective Particle Swarm Optimization (MOPSO) based on Pareto Dominance Approach

This paper presents a comprehensive review of a multiobjective particle swarm optimization (MOPSO) reported in the specialized literature. The success of the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer has motivated researchers to extend the use of bio-inspired technique to other areas. One of them is multi-objective optimization. Multi-objective optimization is a class of problems with solutions that can be evaluated along two or more incomparable or conflicting objectives. These types of problems differ from standard optimization problems in that the end result is not a single \best but rather a set of alternatives, where for each member of the set, no other solution is completely better (the Pareto set). Multi-objective optimization problems occur in many different real-world domains such as automobile design and architecture. A multiobjective particle swarm optimization (MOPSO) method can be used to solve the problem of effective channel selection. General Terms MOPSO and PSO Algorithm

Optimization of micro-grid system using MOPSO

Access to a reliable source of electricity is a basic need for any community as it can improve the living standards characterized via the improvement of healthcare, education, and the local economy at large. There are two key factors to consider when assessing the appropriateness of a micro-grid system, the cost-effectiveness of the system and the quality of service. The tradeoff between cost and reliability of the system is a major compromise in designing hybrid systems. In this way, optimization of a Hybrid Micro-Grid System (HMGS) is investigated. A hybrid wind/PV system with battery storage and diesel generator is used for this purpose. The power management algorithm is applied to the load, and the Multi-Objective Particle Swarm Optimization (MOPSO) method is used to find the best configuration of the system and for sizing the components. A set of recent hourly wind speed data from three meteorological stations in Iran, namely: Nahavand, Rafsanjan, and Khash, are selected and tested for the optimization of HMGS. Despite design complexity of the aforementioned systems, the results show that the MOPSO optimization model produces appropriate sizing of the components for each location. It is also suggested that the use of HMGS can be considered as a good alternative to promote electrification projects and enhance energy access within remote Iranian areas or other developing countries enjoying the same or similar climatic conditions.

A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems

SUMMARY In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO-simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examined for a case study including wind turbines, photovoltaic (PV) panels, diesel generators, batteries, fuel cells, electrolyzer, and hydrogen tanks. Well-known metrics are used to evaluate the generated PF. The average spacing and diversification metrics obtained by the proposed approach are 1386 and 4656, respectively. Additionally, the set coverage metric value shows that at least 67% of Pareto solutions obtained by DMOPSO dominate the solutions resulted by other reported algorithms. By using a sensitivity analysis for the case study, it is found that if the PV panel and wind turbine capital cost are decreased by 50%, the total NPC of the system would be decreased by 18.8 and 3.7%, respectively. Copyright © 2014 John Wiley & Sons, Ltd.

Population Classification in Fire Evacuation: A Multiobjective Particle Swarm Optimization Approach

In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making; and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.

Multi-objective particle swarm optimization method with balanced diversity and convergence

Particle Swarm Optimization(PSO) algorithm is population-based and it is effective for multi-objective optimization problems.For the convergence of the swarm makes the classical algorithm easily converge to local pareto front,the convergence and diversity of the solution are not satisfactory.This paper proposed an independent dynamic inertia weights method for multi-objective particle swarm optimization(DWMOPSO).It changed each particle's inertia weight according to the evolution speed which was calculated by the value of each particle's best fitness in the history.It improved the probability to escape from the local optima.In comparison with Coello's MOPSO through five standard test functions,the solution of the new algorithm has great improvement both in the convergence to the true Pareto front and diversity.

Minimizing Risk Costs through Optimal Responses in NPD Projects

In rapidly changing market environment, firms are investing a lot of time and resources into new product development (NPD) projects to make profit and to obtain competitive advantage. However, failure rate of NPD projects is becoming high due to various internal and external risks which hinder successful NPD projects. To reduce the failure rate, it is critical that risks have to be managed effectively and efficiently through good strategy, and treated by optimal responses to minimize risk cost. Four strategies are adopted to handle the risks in this study. The optimal responses are characterized by high reduction of risk costs with high efficiency. This study suggests a framework to decide the optimal responses considering the core risks, risk costs, response efficiency and response costs for successful NPD projects. Both binary particles warm optimization (BPSO) and multi-objective particle swarm optimization (MOPSO) methods are mainly used in the framework. Although several limitations exist in use for real industries, the frame work shows good strength for handling the risks with highly scientific ways through an example. Keywords—NPD projects, risk cost, strategy, optimal responses, Particle Swarm Optimization.

Multi-objective adaptive Clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty

This paper presents a new multi objective optimization approach based on Adaptive Clonal Selection Algorithm (ACSA) to solve complex Environmental/Economic Dispatch (EED) problem of thermal generators in power system. The proposed methodology also incorporates the power demand equality constraint and ensures various operating constraint limits while solving EED problem. In this algorithm an adaptive Clonal selection principle with non-dominated sorting technique and crowding distance has been used to find and manage Pareto-optimal set. Clonal selection principle is one of the models used to incorporate the behavior of the artificial immune system. The biological principles of clone generation, proliferation and maturation are mimicked and incorporated into this algorithm. To show the effectiveness of the proposed Multi Objective Adaptive Clonal Selection Algorithm (MOACSA) in solving EED problem two types of test systems have been considered with various objectives. These includes an IEEE 30-bus 6 unit test system and an 82-bus 10 unit Indian utility real life power system network for solving EED problem without and with load uncertainty. Simulation results are compared by implementation of three other standard algorithms such as Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Differential Evaluation (MODE) methods.

A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems

In this paper, a new dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO) method is proposed to solve binary-state multi-objective reliability redundancy allocation problems (MORAPs). A combination of penalty function and modification strategies is used to handle the constraints in the MORAPs. A dynamic self-adaptive penalty function strategy is utilized to handle the constraints. A heuristic cost-benefit ratio is also supplied to modify the structure of violated swarms. An adaptive survey is conducted using several test problems to illustrate the performance of the proposed DSAMOPSO method. An efficient version of the epsilon-constraint (AUGMECON) method, a modified non-dominated sorting genetic algorithm (NSGA-II) method, and a customized time-variant multi-objective particle swarm optimization (cTV-MOPSO) method are used to generate non-dominated solutions for the test problems. Several properties of the DSAMOPSO method, such as fast-ranking, evolutionary-based operators, elitism, crowding distance, dynamic parameter tuning, and tournament global best selection, improved the best known solutions of the benchmark cases of the MORAP. Moreover, different accuracy and diversity metrics illustrated the relative preference of the DSAMOPSO method over the competing approaches in the literature.

A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization

In this paper a new hybrid method is proposed for multi-objective optimization problem. In multi-objective particle swarm optimization methods, selecting the global best particle for each particle ...

The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps

In the present study, multi-objective optimization of centrifugal pumps is performed in three steps. In the first step, efficiency (η) and the required net positive suction head (NPSHr) in a set of centrifugal pumps are numerically investigated using commercial software. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained in the second step for modeling of η and NPSHr with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, a multi-objective particle swarm optimization method (MOPSO) is used for Pareto-based optimization of centrifugal pumps considering two conflicting objectives, η and NPSHr. The Pareto results of the MOPSO method are also compared with those of a multi-objective genetic algorithm (NSGA II). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of centrifugal pumps can be discovered by Pareto-based multi-objective optimization of the obtained polynomial metamodels representing η and NPSHr characteristics.

Learning-based ship design optimization approach

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multi-agent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application.

A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization

In this paper a new hybrid method is proposed for multi-objective optimization problem. In multi-objective particle swarm optimization methods, selecting the global best particle for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions. Here, this problem is solved by incorporating charged system search method into the search process of the particle swarm optimization algorithm. In this approach, each particle is guided by its personal best and also resultant force which acted on this particle. This force is the consequence of the attraction field which is created around each archive member, where the magnitude of this force is related to the charge magnitude of the particles and also the distance between them. Each particle is guided by just archive members, which are located in the same region of the objective space as this particle. The proposed method is examined for different test functions and the results are compared to the results of three state-of-art multi-objective algorithms.

A multi-objective optimal power flow using particle swarm optimization

This paper presents a multi-objective optimal power flow technique using particle swarm optimization. Two conflicting objectives, generation cost, and environmental pollution are minimized simultaneously. A multi-objective particle swarm optimization method is used to solve this highly nonlinear and non-convex optimization problem. A diversity preserving technique is incorporated to generate evenly distributed Pareto optimal solutions. A fuzzy membership function is proposed to choose a compromise solution from the set of Pareto optimal solutions. The algorithm is tested on IEEE 30 and 118 bus systems and its effectiveness is illustrated. Copyright © 2010 John Wiley & Sons, Ltd.

Interactive multi-objective particle swarm optimization with heatmap-visualization-based user interface

This article introduces an interactive Multi-Objective Particle Swarm Optimization (MOPSO) method that allows a human decision maker to guide the optimization process based on domain-specific knowledge and problem-specific preferences. This article also presents a novel graphical user interface based on heatmap visualization which, combined with the algorithm, greatly reduces the workload on the user, thereby decreasing unwanted side effects caused by human fatigue. The method was evaluated on a set of standard test problems and the results were compared to those of a non-interactive MOPSO method. To simulate domain-specific preferences and knowledge, the decision maker was instructed to focus the search on a specific region of the Pareto-front. The results demonstrate that the proposed method was able to obtain better solutions than the non-interactive MOPSO method in terms of convergence towards the true Pareto-front and the number and spread of focused solutions.

Multiobjective particle swarm optimization with nondominated local and global sets

In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper presents an approach using two sets of nondominated solutions. The ability of the proposed approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments on well-known non-trivial multiobjective test problems as well as the real-life electric power dispatch problem. The diversity of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed through a comparative study with the reported results in the literature.

Crashworthiness design for functionally graded foam-filled thin-walled structures

Foam-filled thin-wall structures have exhibited significant advantages in light weight and high energy absorption and been widely applied in automotive, aerospace, transportation and defence industries. Unlike existing uniform foam materials, this paper introduces functionally graded foam (FGF) fillers to fill thin-walled structures, aiming to improve crashworthiness. In this novel structure, the foam density varies throughout the depth in a certain gradient. Numerical simulations showed that gradient exponential parameter m that controls the variation of foam density has significant effect on system crashworthiness. In this study, the single and multiobjective particle swarm optimization methods are used to seek for optimal gradient, where response surface models are established to formulate specific energy absorption and peak crushing force. The results yielded from the optimizations indicate that the FGF material is superior to its uniform counterparts in overall crashworthiness. The data has considerable implication in design of FGF materials for optimizing structural crashworthiness.

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.

A multi-objective chaotic particle swarm optimization for environmental/economic dispatch

A multi-objective chaotic particle swarm optimization (MOCPSO) method has been developed to solve the environmental/economic dipatch (EED) problems considering both economic and environmental issues. The proposed MOCPSO method has been applied in two test power systems. Compared with the conventional multi-objective particle swarm optimization (MOPSO) method, for the compromising minimum fuel cost and emission case, the fuel cost and pollutant emission obtained from MOCPSO method can be reduced about 50.08 $/h and 2.95 kg/h, respectively, in test system 1, about 0.02 $/h and 1.11 kg/h, respectively, in test system 2. The MOCPSO method also results in higher quality solutions for the minimum fuel cost case and the minimum emission case in both of the test power systems. Hence, MOCPSO method can result in great environmental and economic effects. For EED problems, the MOCPSO method is more feasible and more effective alternative approach than the conventional MOPSO method.