This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use of particles’ personal bests and offspring for more effective nondomination comparisons. Instead of a single comparison between a particle’s personal best and its offspring, NSPSO compares all particles’ ...

Problem statement: Most of the control engineering problems are characterized by several, contradicting, conflicting objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion and using Pareto-optimal solutions. Approach: Non-Dominated Sorting Particle Swarm Optimization algorithm (NSPSO)...

This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in a day-ahead uniform price spot market using non-dominated sorting particle swarm optimisation (NSPSO). NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximisation and risk (profit variation) minimisation. Monte Carlo (MC) simulation is employed to si...

In this paper, a control technique based on simultaneous regulation of all control coefficients for Unified Power Flow Controller (UPFC) and Power System Stabilizer (PSS) is proposed. Non-Dominated Sorting Particle Swarm Optimization (NSPSO) has been used for parameter optimization. Studies in time and frequency domains for single machine infinite bus power system (SMIB) in different operationa...

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...

To improve the performance, robustness and stability of autotuning proportional integral derivative (PID) parameter, novel stable particle swarm optimization (NSPSO) is proposed in this paper. The NSPSO combination algorithm with new rule to reconsider survival remaining search space for handling instability system. based on proving according Lypunov theorem. Additionally, show method’s superio...

In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating failure probability (Pf). However, existing sampling methods cannot guarantee that candidate samples can approach LSF actively, which lowers accuracy and stability results causes excess computational effort. this paper, a novel samples-generating algori...