A novel optimization booster algorithm

Abstract

Abstract In this paper, a novel meta-heuristic method called the Optimization Booster Algorithm (OBA) is presented. It incorporates existing optimization methods with human-inspired intelligence, which applies particularly while conducting business in exchange markets. A key objective in exchange markets is to increase wealth over time, which is typically the same objective when performing optimizations. Moreover, optimization is about finding a way to increase the fitness value of a system, by spending adequate computation time. The OBA is founded on the core idea that a key reason behind the rapid evolution of human societies compared to the tortoise-like natural evolution is the forward-looking approach. In exchange markets, analysts have learned to make decisions based on forecasted prices, rather than the current prices; and this is an illustrative example of the application of such forward-looking approaches, which form the essence of the OBA. Following extensive numerical experiments, and applications of fourteen well-known heuristic and meta-heuristic methods to solve seventy-one non-linear unconstrained and constrained, single-objective and multi-objective benchmarks, before and after receiving a boost, the OBA performance is investigated. It has proven — in both theory and practice — to quite significantly improve existing optimization methods. In most cases, boosting resulted in much better quality of outputs, while requiring less computation time.