Development and validation of constraints handling in a Differential Evolution optimizer

Research work requires independent, portable optimization tools for many applications, most often for problems where derivability of objective functions is not satisfied. Differential evolution optimization represents an alternative to the more complex, encryption based genetic algorithms. Various packages are available as freeware, but they lack constraints handling, while constrained optimizations packages are commercially available. However, the literature devoted to constraints treatment is significant and the current work is devoted to the implementation of such an optimizer, to be applied in low-fidelity optimization processes. The parameter free penalty scheme is adopted for implementation, and the code is validated against the CEC2006 benchmark test problems and compared with the genetic algorithm in MATLAB. Our paper underlines the implementation of constrained differential evolution by varying two parameters, a predefined parameter for feasibility and the scaling factor, to ensure the convergence of the solution.

Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization

With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity (PD) is an effective way to monitor an algorithm's ability for exploration and exploitation. Through the PD measurement, useful search information can be obtained. PSO with a different topology structure and different boundary constraints handling strategy will have a different impact on particles' exploration and exploitation ability. In this chapter, the phenomenon of particles gets “stuck in” the boundary in PSO and is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques and analyze the impact of these strategies on the algorithm's ability of exploration and exploitation.

Optimization Driven Constraints Handling in Combinatorial Interaction Testing

The combinatorial strategy is useful in the reduction of the number of input parameters into a compact set of a system based on the combinations of the parameters. This strategy can be used in testing the behaviour that takes place when the events are allowed to be executed in an appropriate order. Basically, in the software systems, for the highly configurable system, the input configurations are based on the constraints, and the construction of this idea undergoes various kinds of difficulties. The proposed Jaya-Bat optimization algorithm is developed with the combinatorial interaction test cases in an effective manner in the presence of the constraints. The proposed Jaya-Bat based optimization algorithm is the integration of the Jaya optimization algorithm (JOA) and the Bat optimization algorithm (BA). The experimentation is carried out in terms of average size and the average time to prove the effectiveness of the proposed algorithm. From the results, it is clear that the proposed algorithm is capable of selecting the test cases optimally with better performance.