Evolutionary Computation for Dynamic Optimisation Problems with Different Requirement Satisfaction

In many real-world optimization problems, the search space changes over a period of time. Unlike the case of static optimization, in these time-varying optimization scenarios known as Dynamic Optimization Problems (DOPs), learning from previous evaluations can be beneficial in tackling the current environment under the assumption that the properties of the problems and the position of their optima cannot change significantly between two consecutive environmental changes. There are many approaches to dealing with DOPs, but most research focuses on tracking moving optima or maintaining population diversity. Studies in Evolutionary Dynamic Optimization (EDO) typically allow for many individual evaluations before the environment changes. When the environment changes very quickly, unsolved challenges arise where typical population-based algorithms may be negatively affected by this limiting number of samples. This thesis aims to solve Dynamic Optimization Problems where the environment is fast changing and has a limited budget of how many fitness evaluations an optimizer can take before changing. The work presented in this thesis builds on the essential area of performance evaluation and benchmarking in EDO research. Here, a benchmark problem is created that allows for predicting the movement of optima. Furthermore, a new optimization algorithm that solves fast-changing environments is proposed as a proof-of-concept to test this benchmark. The new benchmark, called Moving Peaks Benchmark with Attractors (MPBA), incorporates an attractor heuristic that attracts peaks to a specific location in the environment. The proposed benchmark is fully flexible, where the dynamics of the attractors and the rate at which a peak is attracted to such attractors can be modified. When these characteristics are adjusted, certain movement styles can be achieved by a peak. A new performance measure that focuses on comparing algorithms that use prediction is also introduced in response to this benchmark. Furthermore, a new optimization algorithm, named Fast Environmental Changes Particle Swarm Optimizer (FEC-PSO) is designed as a proof-of-concept to test the proposed benchmark. This algorithm uses an optimizer to gather information about the environment so that a surrogate model can be built to estimate the location of the global optimum. It is shown that the surrogate model, given a good enough set of samples and an appropriate interpolation function, can accurately represent the environment.