Constrained Multi-objective Particle Swarm Optimization Research Articles

Constraint handling technique based on Lebesgue measure for constrained multiobjective particle swarm optimization algorithm

In this paper, we study on how to achieve balance between minimizing the objectives, satisfying the constraints, and avoiding the population to stuck at locally optimal or locally feasible regions. We propose a new constraints handling technique based on diversity distance measure for constrained multiobjective optimization problems. We innovatively measure all effects of the constraints on the objectives by the interaction effects and direct effects of constraints on the objectives. The interactive effects of constraints on the objective functions are firstly expressed and quantified by using Mahalanobis distance and computing the value of the distance measure by involving a Lebesgue measure. Based on interactive and direct effects, we propose a new modified mechanism of objective space, called objective space modified mechanism based on diversity distance measure, so that the good infeasible solution could be more effectively used to find the optimal solution. The Pull and Push search is used to adaptively adjust the location of the Pareto front, which could preclude the population from being stuck at some locally optimal or locally feasible regions and could decrease time complexity. Comprehensive experiments completed for several benchmark problems demonstrate the competitiveness of the proposed algorithm, in comparison to the existing state-of-art constrained evolutionary multiobjective optimization.

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization

In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach.

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

Work cycle optimization problem of manipulator with revolute joints

In this work the problem of motion modeling and work cycle optimization of manipulator with revolute joints has been considered. The motion equations of the manipulator elements under any spatial work cycle conditions have been formulated. The formulation has been completed by using the classic vector mechanics and Lagrange equations of second kind. The equations of motion of the system have been obtained using commercial software. The chosen motion model for each considered actuator is point-to-point motion model with quasi-trapezoid velocity profile. Additionally, the problem of optimization of a particular work cycle has been presented. The optimization objective has been chosen as minimization of loads (torques) in actuators. The objective function has been formulated using performance indexes and the design variables are rated velocity value and initial time value of work cycle in each considered actuator. The formulated optimization problem has been solved using constrained Multi-Objective Particle Swarm Optimization algorithm. A numerical computation has been completed using specially performed software and results of the computation have been attached to the paperwork.

Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement

Proper placement of Distributed Generation (DG) in distribution system is still very challenging issue for obtaining their maximum potential benefits. This paper proposes a new constrained multi-objective Particle Swarm Optimization (PSO) based Wind Turbine Generation Unit (WTGU) and photovoltaic (PV) array placement approach for power loss reduction and voltage stability improvement of radial distribution system. The paper reflects the effectiveness of WTGU and PV array performance models in DG placement problem formulation. Wind and solar based DGs are operated in different active and reactive power mode and tested on 12-bus, 15-bus, 33-bus and 69-bus radial distribution system. Obtained results are compared with other DG placement technique and proposed method is found to be more effective in terms of voltage stability enhancement and power loss minimization. A novel Voltage Stability Factor (VSF) has been proposed in this paper which can quantify voltage stability levels of buses in the system. Comparing with other voltage stability index and power stability index, developed VSF has emerged as more simple and efficient tool.

The Multi-Objective Optimization for Cycloidal Gear Drive Based on the Elastohydrodynamic Lubrication Theory

For improving the designed quality of the cycloidal gear planetary drive, the paper derives a calculation formula of the minimum film thickness, which is between cycloidal gear teeth, based on elastohydrodynamic lubrication theory and gear geometry. A mathematical model for constrained multi-objective optimization is established and the model satisfies three constrains:maximize minimum film thickness between gear teeth( minimize the reciprocal ), minimize average value of m points’s absolute error value on active section that is between the tooth curves of positive shift optimal combination and tooth curves of rotated angle’s modification, minimize the total volume of gear drive. The paper abandons traditional designed method of the multi-objective optimization , improves two-objective particle swarm optimization and offers a new designed model for constrained three-objective optimization. The example is analyzed and the optimization program is complied using Matlab. The optimization’s process and result shows that the method of improved constrained multi-objective particle swarm optimization could effectively improve the products’ synthesize economical and technical indexes.

Robot path planning in uncertain environment using multi-objective particle swarm optimization

In many real-world applications, workspace of robots often involves various danger sources that robots must evade, such as fire in rescue mission, landmines and enemies in war field. Since it is either impossible or too expensive to get their precise positions, decision-makers know only their action ranges in most cases. This paper proposes a multi-objective path planning algorithm based on particle swarm optimization for robot navigation in such an environment. First, a membership function is defined to evaluate the risk degree of path. Considering two performance merits: the risk degree and the distance of path, the path planning problem with uncertain danger sources is described as a constrained bi-objective optimization problem with uncertain coefficients. Then, a constrained multi-objective particle swarm optimization is developed to tackle this problem. Several new operations/improvements such as the particle update method based on random sampling and uniform mutation, the infeasible archive, the constrained domination relationship based on collision times with obstacles, are incorporated into the proposed algorithm to improve its effectiveness. Finally, simulation results demonstrate the capability of our method to generate high-quality Pareto optimal paths.