Load shedding has been extensively studied because of multiple power system failure occurrences worldwide. Reliable techniques are required to provide rapid and precise load shedding to avert voltage collapse in power networks. Meta-heuristic optimization approaches are currently the widely developed methods because of their robustness and flexibility in dealing with complex and non-linear syst...

Ant colony optimization (ACO) is a novel and promising meta-heuristic for solving hard combinatorial optimization problems, such as travelling salesman[3] [4] [5] [6] [7] [8], quadratic assignment[9] [10] [11] [12] and set covering problem [13]. Unfortunately, according to systems performance evaluation literature, the methods adopted to experimentally validate the current ACO are not enough ac...

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven surge applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues high-dimensionality, they all induce cost in terms network size or computational runtime. work examines the use quaternions, form hypercomplex...

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics to solve difficult computational optimization problems. There are two main types of hyper-heuristics: selective and generative hyper-heuristics. An online selective hyper-heuristic framework manages a set of low level heuristics and aims to choose the best one at any given time using a performance mea...

A heuristic particle swarm optimization (HPSO) is proposed as a solution to one-dimensional cutting stock problem (1D-CSP), which incorporate genetic operators into particle swarm optimization (PSO). In this paper, a heuristic strategy that is based on the results of analysis of the optimal cutting pattern of particles with successful search processes is described, which process a global optimi...