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Heterogeneous particle swarm optimizers (HPSO) add multiple search behaviors to the swarm. This is done by allowing particles to utilize different update equations to each other. Dynamic and adaptive HPSO algorithms allow the particles to change their behaviors during the search. A number of factors come into play when dealing with the different behaviors, one of which is deciding when a particle...
Heterogeneous particle swarm optimizers (HPSO) allow particles to use different update equations, referred to as behaviors, within the swarm. Dynamic HPSOs allow the particles to change their behaviors during the search. These HPSOs alter the exploration/exploitation balance during the search which alters the search behavior of the swarm. This paper introduces a new self-adaptive HPSO and compares...
In spite of substantial research applying evolutionary algorithms and swarm based algorithms to solve dynamic problems, the classification of dynamic environments is missing universal standards. This paper examines the various methods used so far to characterise dynamic optimisation problems and proposes an inclusive classification system. Additionally, a way to generate environments of each type...
Competitive Differential Evolution (CDE) [1] is a multi-population Differential Evolution (DE) algorithm for optimization in dynamic environments. As such, the control parameters present in DE, are also present in CDE. This paper investigates incorporation of three approaches to self-adapting control parameters into CDE. A comparative evaluation of the performance of each approach is used to determine...
The sigmoid function is a widely used, bounded activation function for feedforward neural networks (FFNNs). A problem with using bounded activation functions is that it necessitates scaling of the data to suit the fixed domain and range of the function. Alternatively the activation function itself can be adapted by learning the gradient and range of the function alongside the FFNN weights. The purpose...
This paper investigates the performance of a dynamic heterogeneous particle swarm optimizer (dHPSO) on dynamic unconstrained optimization problems. The results are compared to that of charged and quantum particle swarms, specifically designed for optimization in dynamic environments. It is shown that dHPSO possesses the ability to manage the diversity of the swarm dynamically, allowing it to overcome...
This paper investigates the applicability of swarm-based algorithms to the game of Tetris. This work proposes an approach to the problem in which neural network weight values are optimized using a particle swarm optimization (PSO) algorithm. Such an approach has not previously been demonstrated as feasible for Tetris. The reported experimental results show the learning progress of the algorithm, as...
The purpose of this paper is to investigate the use of meta-heuristics as low-level heuristics in a hyper-heuristic framework. A novel multi-method hyper-heuristic algorithm which makes use of a number of common meta-heuristics is presented. Algorithm performance is evaluated on a diverse set of real parameter benchmark problems and meaningful conclusions are drawn with respect to the selection of...
The purpose of this paper is to investigate the overfitting behavior of particle swarm optimization (PSO) trained neural networks. Neural networks trained with PSOs using the global best, local best and Von Neumann information sharing topologies are investigated. Experiments are conducted on five classification and five time series regression problems. It is shown that differences exist in the degree...
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