Negotiation over limited resources, as a way for the agents to reach agreement, is one of the significant topics in Multi-Agent Systems (MASs). Most of the models proposed for negotiation suffer from different limitations in the number of the negotiation parties and issues as well as some constraining assumptions such as availability of unlimited computational resources and complete information...

Scheduling tasks is one of the core steps to effectively exploit the capabilities of distributed or parallel computing systems. In general, scheduling is an NP-hard problem. Most existing approaches for scheduling deal with a single objective only. This paper presents a multi-objective scheduling algorithm based on particle swarm optimization (PSO). In this paper a non-dominated sorting particl...

This paper proposes a homogeneous distributed computing (HDC) framework for multi-objective evolutionary algorithm (MOEA). In this framework, multiple processors divide a work into several pieces and carry them out in parallel. Every processor does its task in a homogeneous way so that the overall procedure becomes not only faster but also fault-tolerant and independent to the number of process...

Pareto Based Multi Objective Optimization Algorithms, including Multi-Objective Particle Swarm Optimization (MOPSO), face several problems when applied to Multi Objective Optimization Problems with a large number of objective functions, especially the deterioration of the search ability. In the literature some techniques were proposed to overcome these limitations, among them the modification o...

In this paper, we propose a dynamic, non-dominated sorting, multiobjective particle-swarm-based optimizer, named Hierarchical Non-dominated Sorting Particle Swarm Optimizer (H-NSPSO), for memory usage optimization in embedded systems. It significantly reduces the computational complexity of others MultiObjective Particle Swarm Optimization (MOPSO) algorithms. Concretely, it first uses a fast no...

Nowadays, the core of the Particle Swarm Optimization (PSO) algorithm has proved to be reliable. However, faced with multi-objective problems, adaptations are needed. Deeper researches must be conducted on its key steps, such as solution set management and guide selection, in order to improve its efficiency in this context. Indeed, numerous parameters and implementation strategies can impact on...

This paper presents a new approach to treat reactive power (VAr) planning problem using multi-objective evolutionary algorithms. Specifically, Strength Pareto Evolutionary Algorithm (SPEA) and Multi-Objective Particle Swarm Optimization (MOPSO) approaches have been developed and successfully applied. The overall problem is formulated as a nonlinear constrained multi-objective optimization probl...

There is an increasing interest in renewable and green energy sources and the integration of Distributed Generation DG into the power grid system. The problem of optimal capacitor allocation in electric distribution systems involves maximizing energy utilization, feeder loss reduction, and power factor correction. The feeder loss can be separated into two parts based on the active and reactive ...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...