Jingxuan Wei

A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design

Designing efficient algorithms for difficult multi-objective optimization problems is a very challenging problem. In this paper a new clustering multi-objective evolutionary algorithm based on orthogonal and uniform design is proposed. First, the orthogonal design is used to generate initial population of points that are scattered uniformly over the feasible solution space, so that the algorithm can evenly scan the feasible solution space once to locate good points for further exploration in subsequent iterations. Second, to explore the search space efficiently and get uniformly distributed and widely spread solutions in objective space, a new crossover operator is designed. Its exploration focus is mainly put on the sparse part and the boundary part of the obtained non-dominated solutions in objective space. Third, to get desired number of well distributed solutions in objective space, a new clustering method is proposed to select the non-dominated solutions. Finally, experiments on thirteen very difficult benchmark problems were made, and the results indicate the proposed algorithm is efficient.

Hyper rectangle search based particle swarm algorithm for dynamic constrained multi-objective optimization problems

In the real world, many optimization problems are dynamic constrained multi-objective optimization problems. This requires an optimization algorithm not only to find the global optimal solutions under a specific environment but also to track the trajectory of the varying optima over dynamic environments. To address this requirement, a hyper rectangle search based particle swarm algorithm is proposed for such problems. This algorithm employs a hyper rectangle search to predict the optimal solutions (in variable space) of the next time step. Then, a PSO based crossover operator is used to deal with all kinds of constraints appearing in the problems when the time step (environment) is fixed. This algorithm is tested and compared with two well known algorithms on a set of benchmarks. The results show that the proposed algorithm can effectively track the varying Pareto fronts over time.

A novel particle swarm optimization algorithm with local search for dynamic constrained multi-objective optimization problems

In the real world, many optimization problems are dynamic constrained multi-objective optimization problems. This requires an optimization algorithm not only to find the global optimal solutions under a specific environment but also to track the trajectory of the varying optima over dynamic environments. To address this requirement, this paper proposes a novel particle swarm optimization algorithm for such problems. This algorithm employs a new points selection strategy to speed up evolutionary process, and a local search operator to search optimal solutions in a promising subregion. The new algorithm is examined and compared with two wellknown algorithms on a sequence of benchmark functions. The results show that the proposed algorithm can effectively track the varying Pareto fronts over time. The proposed developments are effective individually, but the combined effect is much better for the test functions.

Simplex model based evolutionary algorithm for dynamic multi-objective optimization

Most real-world problems involve objectives, constraints and parameters which constantly change with time. Treating such problems as static problems requires knowledge of the prior time but the computational cost is still high. In this paper, a simplex model based evolutionary algorithm is proposed for dynamic multi-objective optimization, which uses a modified simplex model to predict the optimal solutions (in variable space) of the next time step. Thereafter, a modified evolutionary algorithm which borrows ideas from particle swarm optimization is applied to solve multi-objective problems when the time step is fixed. This method is tested and compared on a set of benchmarks. The results show that the method can effectively track varying Pareto fronts over time.

A novel multi-objective PSO algorithm for constrained optimization problems

A new approach is presented to handle constrained optimization by using PSO algorithm. It neither uses any penalty function in the proposed PSO algorithms. The new technique treats constrained optimization as a two-objective optimization, one objective is original objective function, and the other is the degree violation of constraints. As we prefer the second objective, a new crossover operator is designed based on the three-parent crossover operator, which will lead the degree violation of constraints to zero. Then, in order to keep the diversity of the swarm and escape from the local optimum easily, we design a dynamically changing inertia weight. The simulation results indicate the proposed algorithm is effective.

A memetic particle swarm optimization for constrained multi-objective optimization problems

In this paper, a new memetic algorithm for constrained multi-objective optimization problems is proposed, which combines the global search ability of particle swarm optimization with an attraction based local search operator for directed local fine-tuning. Firstly, a new particle updating strategy is proposed based on the concept of uncertain personal-best to deal with the problem of premature convergence. Secondly, an attraction based local search operator is proposed to find good local search direction for the particles. Finally, the convergence of the algorithm is proved. The proposed algorithm is examined and compared with two well known existing algorithms on five benchmark test functions. The results suggest that the new algorithm can evolve more good solutions, and the solutions are more widely spread and uniformly distributed along the Pareto front than the two existing methods. The proposed two developments are effective individually, but the combined effect is much better for these constrained multi-objective optimization problems.

A Dynamical Particle Swarm Algorithm with Dimension Mutation

In this paper, a dynamical particle swarm algorithm with dimension mutation is proposed. First, we design a dynamically changing inertia weight based on the degree of both the particle diversity and the improvement of the best solutions in the successive generations. By using this inertia weight the algorithm can more easily keep the diversity of the population, improve the convergent speed. Second, in order to escape from the local optimum easily, a dimension mutation operator is designed. This mutation operator can easily jump out the local optimum from the dimension with the minimal convergence degree (i.e., the dimension in which the particles focus to the center of the search region most). Finally, the simulation experiments are made and the results indicate the high efficiency of the proposed algorithm

Attraction based PSO with sphere search for dynamic constrained multi-objective optimization problems

Developing efficient algorithms for dynamic constrained multi-objective optimization problems (DCMOPs) is very challenging. This paper describes an attraction based particle swarm optimization (PSO) algorithm with sphere search for such problems. A dynamic constrained multi-objective optimization problem is transformed into a series of static constrained multi-objective optimization problems by dividing the time period into several equal intervals. To speed up optimization process and reuse the information of Pareto optimal solutions obtained from previous time, a new method based on sphere search is proposed to generate the initial swarm for the next time interval. To deal with the transformed problem effectively, a new particle comparison strategy is proposed for handling constraints in the problem. A local search operator based on the concept of attraction is introduced for finding good search directions of the particles. The results show that the proposed algorithm can effectively track the varying Pareto fronts with time.

New model and genetic algorithm for multi-installment divisible-load scheduling

The era of big data computing is coming. As scientific applications become more data intensive, finding an efficient scheduling strategy for massive computing in parallel and distributed systems has drawn increasingly attention. Most existing studies considered single-installment scheduling models, but very few literature involved multi-installment scheduling, especially in heterogeneous parallel and distributed systems. In this paper, we proposed a new model for periodic multi-installment divisible-load scheduling in which the make-span of the workload is minimized, and a genetic algorithm was designed to solve this model. Finally, experimental results show the effectiveness and efficiency of the proposed algorithm.

A new non-redundant objective set generation algorithm in many-objective optimization problems

Among the many-objective optimization problems, there exists a kind of problem with redundant objectives, it is possible to design effective algorithms by removing the redundant objectives and keeping the non-redundant objectives so that the original problem becomes the one with much fewer objectives. In this paper, a new non-redundant objective set generation algorithm is proposed. To do so, first, a multi-objective evolutionary algorithm based decomposition is adopted to generate a small number of representative non-dominated solutions widely distributed on the Pareto front. Then, the conflicting objective pairs are identified through these non-dominated solutions, and the non-redundant objective set is determined by these pairs. Finally, the experiments are conducted on a set of benchmark test problems and the results indicate the effectiveness and efficiency of the proposed algorithm.