In this paper, we propose to integrate particle swarm optimization algorithm into cultural algorithms frame to develop a more efficient cultural particle swarm algorithms (CPSA) for constrained multi-objective optimization problem. In our CPSA, the population space of cultural algorithms consists of n+1 subswarms which are used to search for the n single-objective optimums and an additional mul...

Many real-world optimization problems have multiple objectives that have to be optimized simultaneously. In multi-objective optimization problems, the major challenge is to find the set of solutions that achieve the best compromise with regard to the whole set of objectives. Although a great deal of effort has been devoted to solving multi-objective optimization problems, the problem is still o...

the dogleg severity is one of the most important parameters in directional drilling. improvement of these indicators actually means choosing the best conditions for the directional drilling in order to reach the target point. selection of high levels of the dogleg severity actually means minimizing well trajectory, but on the other hand, increases fatigue in drill string, increases torque and d...

in this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classifypatterns of different classes in the feature space. the introduced mutation operators and chaotic sequences allows us to overcomethe problem of early convergence into a local minima associated with particle swarm optimization algorithms. that is, the mutationope...

Aiming at the multi-objective essence of optimal audio watermarking problem, we propose a novel audio watermarking framework in this paper, which can optimally balance all conflicting objectives of the problem, fidelity and robustness against different attacks. In the proposed framework, a multi-objective particle swarm optimization technique based on fitness sharing is applied to search optima...

We present a novel GPU-accelerated per-pixel inverse rendering optimization algorithm based on Particle Swarm Optimization (PSO). Our algorithm estimates the per-pixel scene attributes—including reflectance properties—of a 3D model, and is fast enough to do in situ visualization of the optimization in real-time. The algorithm’s high parallel efficiency is demonstrated through our GPU/GLSL shade...

This paper models the process of a recommender system as a multiobjective optimization problem, a discrete particle swarm optimization framework is established and has been integrated into multiobjective optimization, consequently, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the modeled optimization problem. Each run of the current mainstream recommender...

Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm ...

We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approac...