You can also try diversification techniques such as niching methods or Thresheld Convergence.

Conference Paper Particle Swarm Optimization with Thresheld Convergence

Article A Review of Thresheld Convergence

Genetic algorithm with migration added. This way every few generations (generally generation numbers in multiples of tens) will have new incoming chromosomes that did not evolve with the current ones. This way mating with the new "immigrants" take the current members that are stuck in local optima away from their current positions.

The first thing I would try is to increase the weight assigned to the random movement behaviour and see if that increases diversification. This could be done adaptively, whenever the swarm converges too much. For this case, you would need to implement some function that measures the diversity of the swarm.

Hi,

I think if you adaptively change inertia weight, cognitive and social parameters you can avoid stuck into local minima. Another suggestion to add position update mechanism like Wind Dispersion, to increase the swarm diversity. 

you can go for parameter adaptive methods in PSO by which this problem can be solved

The algorithm described in the following paper of ours may be useful in your work. Further, we have an Excel-based program for BBPSO;, which can be obtained by contact me.

Zhang H., Kennedy D.D., Rangaiah G.P. and Bonilla-Petriciolet A., Novel Bare-Bones Particle Swarm Optimization and its Performance for Modeling Vapor-Liquid Equilibrium Data, Fluid Phase Equilibria, 301, 33-45 (2011).

An approach that is also widely used is to perturb the individuals every n iterations. This some times is applied to all individuals or a percentage of them. Such perturbations could be derived from different random number distributions.

Use of chaotic sequence can escape particles from trapping into local minima.

There are many techniques like mutation, diversity control and parameter adaptation.

You may refer the following paper (Matlab codes are available from me)：

http://www.sciencedirect.com/science/article/pii/S0957417414003418

In addition, you may try some new optimization algorithms, such as Across neighborhood search (ANS) .

http://www.sciencedirect.com/science/article/pii/S0020025515007069

PSO codes in MATLAB

https://www.researchgate.net/publication/296636431_Codes_in_MATLAB_for_Particle_Swarm_Optimization?ev=prf_pub

Code Codes in MATLAB for Particle Swarm Optimization

Kindly, go through the following publication.

Y. del Valle, G. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, R. Harley, Particle swarm optimization: Basic concepts, variants and applications in power systems, IEEE Transactions on Evolutionary Computation, 12 (2) (2008) 171-195.

I hope it will be very helpful to you.

https://www.researchgate.net/publication/297245624_Particle_Swarm_Optimization_Algorithm_and_its_Codes_in_MATLAB

Research Particle Swarm Optimization: Algorithm and its Codes in MATLAB

https://www.researchgate.net/publication/280244004_A_comparative_study_of_metaheuristic_optimization_approaches_for_directional_overcurrent_relays_coordination

Article A comparative study of metaheuristic optimization approaches...