Multi-Strategy Coevolving Aging Particle Optimization

Abstract

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

Progressive preference articulation for decision making in multi-objective optimisation problems

“…In [22] an interactive algorithm based on R-NSGA-II is proposed, and in [23] R-NSGA-II is modified by integrating a stochastic local search in a memetic fashion, see [47], [10], [32], and [72].…”

A sinusoidal differential evolution algorithm for numerical optimisation

“…This procedure is used in the same way as described in [40]. This statistics is based on the notion of average ranking; so only mean values of best errors to the optimum over 51 runs are needed.…”

“…The Holm-Bonferroni procedure is used with the same parameters used in [40]. The null hypothesis is set to: 'the proposed SinDE has the same performance as the j-th algorithm', where j is the algorithm in question (SMADE, DEcfbLS, CMAES-RIS, etc.).…”

A hybrid optimization approach for sustainable process planning and scheduling

“…SA can identify a good solution quickly but may fluctuate around the local optima due to the lack of the memory mechanism. Additionally, several swarm intelligence algorithms such as ACO [12], particle swarm optimization (PSO) [17,18,45,48,52,53] and HBMO [51], were applied to process planning and scheduling. Compared with the other most known heuristic algorithms such as GA, SA and ACO, HBMO has a better performance in computational effectiveness and stability.…”