Sanyou Zeng

Opposition-based particle swarm algorithm with cauchy mutation

Particle swarm optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima. This paper presents an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and avoid premature convergence. The proposed method employs opposition-based learning for each particle and applies a dynamic Cauchy mutation on the best particle. Experimental results on many well- known benchmark optimization problems have shown that OPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization.

A Hybrid Particle Swarm Algorithm with Cauchy Mutation

Particle swarm optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima because the particles could quickly get closer to the best particle. At such situations, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by adding a Cauchy mutation on the best particle so that the mutated best particle could lead all the rest of particles to the better positions. Experimental results on many well-known benchmark optimization problems have shown that HPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization

An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints

In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the selection process. Then, the orthogonal design and the statistical optimal method are generalized to MOPs, and a new type of multi-objective evolutionary algorithm (MOEA) is constructed. In this framework, an original niche evolves first, and splits into a group of sub-niches. Then every sub-niche repeats the above process. Due to the uniformity of the search, the optimality of the statistics, and the exponential increase of the splitting frequency of the niches, OMOEA uses a deterministic search without blindness or stochasticity. It can soon yield a large set of solutions which converges to the Pareto-optimal set with high precision and uniform distribution. We take six test problems designed by Deb, Zitzler et al., and an engineering problem (W) with constraints provided by Ray et al. to test the new technique. The numerical experiments show that our algorithm is superior to other MOGAS and MOEAs, such as FFGA, NSGAII, SPEA2, and so on, in terms of the precision, quantity and distribution of solutions. Notably, for the engineering problem W, it finds the Pareto-optimal set, which was previously unknown.

Multi-population methods in unconstrained continuous dynamic environments

The multi-population method has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing peaks simultaneously. However, to make this approach efficient, several crucial challenging issues need to be addressed, e.g., how to determine the moment to react to changes, how to adapt the number of populations to changing environments, and how to determine the search area of each population. In addition, several other issues, e.g., communication between populations, overlapping search, the way to create populations, detection of changes, and local search operators, should be also addressed. The lack of attention on these challenging issues within multi-population methods hinders the development of multi-population based algorithms in dynamic environments. In this paper, these challenging issues are comprehensively analyzed by a set of experimental studies from the algorithm design point of view. Experimental studies based on a set of popular algorithms show that the performance of algorithms is significantly affected by these challenging issues on the moving peaks benchmark.

Particle swarm optimisation with simple and efficient neighbourhood search strategies

This paper presents a novel particle swarm optimiser (PSO) called PSO with simple and efficient neighbourhood search strategies (NSPSO), which employs one local and two global neighbourhood search strategies. By this way, one strong and two weak locality perturbation operators are embedded in the standard PSO. The NSPSO consists of two main steps. First, for each particle, three trail particles are generated by the mentioned three neighbourhood search strategies, respectively. Then, the best one among the three trail particles is selected to compete with the current particle, and the fitter one is accepted as a current particle. In order to verify the performance of NSPSO, it experimentally has been tested on 12 unimodal and multimodal benchmark functions. The results show that NPSO significantly outperforms other seven PSO variants.

An efficient multi-objective evolutionary algorithm: OMOEA-II

An improved orthogonal multi-objective evolutionary algorithm (OMOEA), called OMOEA-II, is proposed in this paper. Two new crossovers used in OMOEA-II are orthogonal crossover and linear crossover. By using these two crossover operators, only small orthogonal array rather than large orthogonal array is needed for exploiting optimal in the global space. Such reduction in orthogonal array can avoid exponential creation of solutions of OMOEA and improve the performance in robusticity without degrading precision and distribution of solutions. Experimental results show that OMOEA-II can solve problems with high dimensions and large number of local Pareto-optimal fronts better than some existing algorithms recently reported in the literatures.

An improved Particle Swarm Optimization with adaptive jumps

Particle swarm optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima. This paper presents an improved PSO with adaptive jump. The proposed method combines a novel jump strategy and an adaptive Cauchy mutation operator to help escape from local optima. The new algorithm was tested on a suite of well-known benchmark functions with many local optima. Experimental results were compared with some similar PSO algorithms based on Gaussian distribution and Cauchy distribution, and showed better performance on those test functions.

Particle Swarm Optimization with a Novel Multi-Parent Crossover Operator

Particle swarm optimization (PSO) shares many similarities with evolutionary algorithms (EAs), while the standard PSO does not use any evolution operators such as crossover and mutation. This paper presents a hybrid PSO algorithm to inherit some excellent characteristics of advanced evolutionary computation techniques. The proposed method employs a novel multi-parent crossover operator and a self-adaptive Cauchy mutation operator to help escape from local optima. Experimental results on a suit of well-known benchmark functions with many local minima have shown that the proposed method could successfully deal with those difficult multimodal optimization problems.

Dynamic multi-objective differential evolution for solving constrained optimization problem

Dynamic constrained multi-objective differential evolution(DCMODE) is designed for solving constrained optimization problem(COP). Main feature presented in this paper is to construct dynamic multi-objective optimization problem(DMOP) from COP. The two evolved objectives are original function objective and violation objective. Constraints are controlled by dynamic environments, where the relaxed constraints boundaries are gradually tightened to original boundaries. After this dynamic process, DMOP solutions are close to COP solution. This new algorithm is tested on benchmark problems of special session at CEC2006 with 100% success rates of all problems. Compared with several state-of-the-art DE variants referred in this paper, our algorithm outperforms or performs similarly to them. The satisfactory results suggest that it is efficient and generic when handling inequality/equality constraints.

Dynamic constrained multi-objective model for solving constrained optimization problem

Constrained optimization problem (COP) is skillfully converted into dynamic constrained multi-objective optimization problem (DCMOP) in this paper. Then dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) can be used to solve the COP problem by solving the DCMOP problem. Seemingly, a complex DCMOEA algorithm is used to solve a relatively simple COP problem. However, the DCMOEA algorithm can adopt Pareto domination to achieve a good tradeoff between fast converging and global searching, and therefore a DCMOEA algorithm can effectively solve a COP problem by solving the DCMOP problem. An instance of DCMOEA was used to to solve 13 widely used constraint benchmark problems, The experimental results suggest it outperforms or performs similarly to other state-of-the-art algorithms referred to in this paper. The efficient performance of the DCMOEA algorithm shows, to some extend, the DCMOP model works well.