The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the smallest possible computational burden. Particle Swarm Optimization has attracted the interest of researchers due to its simplicity, effectiveness and efficiency in solving numerous single-objective optimization problems. Up-to-date, the...

The problem of coordinating construction vehicles in formations is tackled using the particle swarm optimization algorithm. Coordinated multi-vehicles are anticipated to outperform single vehicles in terms of economy and flexibility. A possible approach is to apply control theoretic methods to vehicle coordination but this may require complicated system models to be employed. The particle swarm...

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS2Os, which extend the single population particl...

For improving the equalization performance of higher-order QAM signals, orthogonal Wavelet transform dynamic Weighted Multi-Modulus blind equalization Algorithm based on the Dynamic Particle Swarm Optimization(DPSO-WWMMA) is proposed. In this proposed algorithm, dynamic particle swarm optimization algorithm and orthogonal wavelet transform are introduced into dynamic Weighted Multi-Modulus blin...

Swarm robotics is focused on creating intelligent systems from large number of simple robots. The majority of nowadays robots are bound to operations within mono-modal locomotion (i.e. land, air or water). However, some animals have the capacity to alter their locomotion modalities to suit various terrains, operating at high levels of competence in a range of substrates. One of the most signifi...

The control of nonlinear chaotic system and the estimation of parameters is a vital issue in nonlinear science. Studies on parameter estimation for chaotic systems have been investigated recently. A variant of Particle Swarm Optimization (PSO) known as Chaotic Multi Swarm Particle Swarm Optimization (CMS-PSO) is proposed which is inspired from the metaphor of ecological co-habitation of species...

Supply chain scheduling is the very heartbeat of all supply chain systems. Optimization of the supply chain scheduling can help increase agility while reduce the running cost of corporations. Complying with its structure, supply chain of the networked manufacturing has the characteristics of distributed, multi plants and multi stages. In this paper, a thorough research was first done on the dec...

In this paper, a BCC-DPSO scheduling algorithm is proposed to solve multi-objective optimization problem for task scheduling on Network-on-Chip (NoC). In our proposal, the relative advantage of the solution is evaluated by calculating its efficiency using BCC model in Data Envelopment Analysis (DEA), and the referred-time method is introduced to rank the BCC-efficient solution. Moreover, a sub-...

We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm...