Junhua Li

• Principal Investigator at Nanchang Hangkong University

There exist many multi-objective optimization problems (MOPs) containing several inequality and equality constraints in practical applications, which are known as CMOPs. CMOPs pose great challenges for existing multi-objective evolutionary algorithms (MOEAs) since the difficulty in balancing the objective minimization and constraint satisfaction. W...

Although multiobjective particle swarm optimizers (MOPSOs) have performed well on multiobjective optimization problems (MOPs) in recent years, there are still several noticeable challenges. For example, the traditional particle swarm optimizers are incapable of correctly discriminating between the personal and global best particles in MOPs, possibl...

As pointed out in recent studies, most evolutionary algorithms have shown their promise in dealing with many-objective optimization problems (MaOPs). However, the ability to balance convergence and diversity and the scalability of objectives are still far from perfect. To address these issues, this paper proposes a strengthened diversity indicator...

In real-world environments, vehicle travel and service time will be affected by unpredictable factors and present a random state. Because of this situation, this paper proposes the vehicle routing problem with soft time windows and stochastic travel and service time (SVRP-STW). The probability distribution of vehicle travel and service time are int...

Indicator based many-objective evolutionary algorithms generally introduce the performance indicator as the selection criterion in environmental selection. In the calculation of some indicators, the reference points as sampled points on Pareto fronts are very important for their calculation. However, it is difficult to obtain good reference points...

In recent years, a variety of multi-objective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, the performance of most existing MOEAs is sensitive to the Pareto front (PF) shapes of the problem to be solved, and it is difficult for these algorithms to manage diversity on various types of P...

The development of algorithms to solve Many‐objective optimization problems (MaOPs) has attracted significant research interest in recent years. Solving various types of Pareto front (PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm (RMEA) is proposed for many‐objective optimization. The archive i...

Recent studies have shown difficulties in balancing convergence and diversity for many-objective optimization problems with various types of Pareto fronts. This paper proposes an adaptive reference vector based evolutionary algorithm for many-objective optimization, termed as ARVEA. The ARVEA develops a reference vector adaptation method, which can...

Optimization in noisy environments is regard as a favorite application domains of genetic algorithms. Different methods for reducing the influence of noise are presented and discussed. A new fitness evaluation method is proposed that reevaluates all survival individuals each generation. Compared with re-sampling and population sizing, the new evalu...

To extend GA's application, that is important to study on genetic algorithm under noise environment. This paper firstly described the noise environment of the GA, analyzed the effect on GA of noise; then two indexes were proposed to evaluate the performance of GA in the noisy environment, CBMPGA was proposed for the noisy optimization, the numerica...

Niche genetic algorithm (NGA) is superior to genetic algorithm (GA) in multiple hump function optimization. NGA could search all global optimums of multiple hump function in a running. It is a class of parallel evolutionary method which suppresses genetic drift by forming stable subpopulations to maintain population diversity. To algorithm populati...

In this paper, a new genetic algorithm with two species is proposed. Our dual species genetic algorithm (DSGA) composes of two subpopulation that constitute of same size individuals. The subpopulations have different characteristics, such as crossover probability and mutation operator. In one subpopulation, the parents with higher similarity are cr...

Niche genetic algorithm is superior to genetic algorithm in multiple hump function optimization. However, there is lack of theory to determine the parameter of niche distance, so the algorithm's application is limited. This paper presents a new approach to determine the niche distance parameter, which is based on genetic algorithm. According to the...