  In this paper, we propose a simple global optimisation algorithm inspired by
Pareto's principle. This algorithm samples most of its solutions within
prominent search domains and is equipped with a self-adaptive mechanism to
control the dynamic tightening of the prominent domains while the greediness of
the algorithm increases over time (iterations). Unlike traditional
metaheuristics, the proposed method has no direct mutation- or crossover-like
operations. It depends solely on the sequential random sampling that can be
used in diversification and intensification processes while keeping the
information-flow between generations and the structural bias at a minimum. By
using a simple topology, the algorithm avoids premature convergence by sampling
new solutions every generation. A simple theoretical derivation revealed that
the exploration of this approach is unbiased and the rate of the
diversification is constant during the runtime. The trade-off balance between
the diversification and the intensification is explained theoretically and
experimentally. This proposed approach has been benchmarked against standard
optimisation problems as well as a selected set of simple and complex
engineering applications. We used 26 standard benchmarks with different
properties that cover most of the optimisation problems' nature, three
traditional engineering problems, and one real complex engineering problem from
the state-of-the-art literature. The algorithm performs well in finding global
minima for nonconvex and multimodal functions, especially with high dimensional
problems and it was found very competitive in comparison with the recent
algorithmic proposals. Moreover, the algorithm outperforms and scales better
than recent algorithms when it is benchmarked under a limited number of
iterations for the composite CEC2017 problems.
