Xiaoyan Sun

Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects

This paper presents an efficient hybrid particle swarm optimization algorithm to solve dynamic economic dispatch problems with valve-point effects, by integrating an improved bare-bones particle swarm optimization (BBPSO) with a local searcher called directionally chaotic search (DCS). The improved BBPSO is designed as a basic level search, which can give a good direction to optimal regions, while DCS is used as a fine-tuning operator to locate optimal solution. And an adaptive disturbance factor and a new genetic operator are also incorporated into the improved BBPSO to enhance its search capability. Moreover, a heuristic handing mechanism for constraints is introduced to modify infeasible particles. Finally, the proposed algorithm is applied to the 5-, 10-, 30-unit-test power systems and several numerical functions, and a comparative study is carried out with other existing methods. Results clarify the significance of the proposed algorithm and verify its performance.

Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis

Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation.

Solving interval multi-objective optimization problems using evolutionary algorithms with preference polyhedron

Multi-objective optimization (MOO) problems with interval parameters are popular and important in real-world applications. Previous evolutionary optimization methods aim to find a set of well-converged and evenly-distributed Pareto-optimal solutions. We present a novel evolutionary algorithm (EA) that interacts with a decision maker (DM) during the optimization process to obtain the DMs most preferred solution. First, the theory of a preference polyhedron for an optimization problem with interval parameters is built up. Then, an interactive evolutionary algorithm (IEA) for MOO problems with interval parameters based on the above preference polyhedron is developed. The algorithm periodically provides a part of non-dominated solutions to the DM, and a preference polyhedron, based on which optimal solutions are ranked, is constructed with the worst solution chosen by the DM as the vertex. Finally, our method is tested on two bi-objective optimization problems with interval parameters using two different value function types to emulate the DMs responses. The experimental results show its simplicity and superiority to the posteriori method.

Multi-objective PSO Algorithm for Feature Selection Problems with Unreliable Data

Feature selection is an important data preprocessing technique in classification problems. This paper focuses on a new feature selection problem, in which sampling data of different features have different reliability degree. First, the problem is modeled as a multi-objective optimization. There two objectives should be optimized simultaneously: reliability and classifying accuracy of feature subset. Then, a multi-objective feature selection method based on particle swarm optimization, called JMOPSO, is proposed by incorporating several effective operators. Finally, experimental results suggest that the proposed JMOPSO is a highly competitive feature selection method for solving the feature selection problem with unreliable data.

Application of variational granularity language sets in interactive genetic algorithms

An interactive genetic algorithm with evaluating individuals using variational granularity was presented in this study to effectively alleviate user fatigue. In this algorithm, multiple language sets with different evaluation granularities are provided. The diversity of a population described with the entropy of its gene meaning units is utilized to first choose parts of appropriate language sets to participate in evaluating the population. A specific language set for evaluating an individual is further selected from these sets according to the distance between the individual and the current preferred one. The proposed algorithm was applied to a curtain evolutionary design system and compared with previous typical ones. The empirical results demonstrate the strengths of the proposed algorithm in both alleviating user fatigue and improving the efficiency in search.

Hybrid many-objective particle swarm optimization set-evolution

Many-objective optimization problems (MaOPs) are difficult to be solved by those traditional evolutionary multi-objective (EMO) algorithms due to the loss of enough selection pressure. The indicator-based EMO developed for MaOPs has been proved to be effective, however, it has not been well combined with the framework of particle swarm optimization (PSO). Therefore, we here propose a hybrid indicator-based PSO for MaOPs, in which the sets of solutions are evolved as an “individual”. First, the sets-oriented PSO is designed to perform the evolution on the sets. The global and local best particles are well explored by considering the performance of the evolution and the computational cost. Then, the solutions in some selected sets are further evolved by a modified mutation to approximate to the true Pareto set in the original MaOP space. The proposed algorithm is experimentally validated on some benchmark MaOPs and its merit is empirically demonstrated by comparing to indicator-based evolutionary genetic algorithms and NSGAII.

A reference points-based evolutionary algorithm for many-objective optimization

Many-objective optimization problems are common in real-world applications, few evolutionary optimization methods, however, are suitable for them up to date due to their difficulties. We proposed a reference points-based evolutionary algorithm (RPEA) to solve many-objective optimization problems in this study. In RPEA, a series of reference points with good performances in convergence and distribution are generated according to the current population to guide the evolution. Furthermore, superior individuals are selected based on the assessment of each individual by calculating the distances between the reference points and the individual in the objective space. The algorithm was applied to four benchmark optimization problems and compared with NSGA-II and HypE. The results experimentally demonstrate that the algorithm is strengthened in obtaining Pareto optimal set with high performances.

Surrogate models for user's evaluations base on weighted support vector machine in IGAs

Interactive genetic algorithms (IGAs) are effective methods of tackling optimization problems involving qualitative indices by incorporating a users evaluations into traditional genetic algorithms. The problem of user fatigue resulting from the users evaluations, however, has a negative influence on the performance of these algorithms. Substituting the users evaluations with various surrogate models is beneficial to alleviate user fatigue. Previous studies, however, have not taken full advantage of information provided by samples obtained earlier when constructing or updating these models. We focus on the issue of user fatigue in this study, and present a novel method of effectively alleviating user fatigue by substituting the users evaluations with a weighted support vector machine (WSVM) and by incorporating it with the mechanism of transfer learning. The proposed method is applied to the fashion evolutionary design system and compared with previous effective IGAs. The experimental results confirm the advantage of the proposed method in both alleviating user fatigue and improving the precision of the surrogate model.

Optimizing interval multi-objective problems using IEAs with preference direction

Interval multi-objective optimization problems (MOPs) are popular and important in real-world applications. We present a novel interactive evolutionary algorithm (IEA) incorporating an optimization-cum-decision-making procedure to obtain the most preferred solution that fits a decision-maker (DM)s preferences. Our method is applied to two interval MOPs and compared with PPIMOEA and the posteriori method, and the experimental results confirm the superiorities of our method.