The paper presents a novel Modulated Power Filter and Compensator (MPFC) scheme for voltage stability, energy conservation, loss reduction, power factor correction, and power quality enhancement for electric distribution systems based on Multi-Objective Particle Swarm Optimisation (MOPSO). The MPFC scheme was developed by the first author to vary the shunt power filter equivalent admittance, mo...

This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the mult...

This paper presents an application of an evolutionary optimization algorithm for multiobjective analysis for reservoir operations and planning. A multiobjective particle swarm optimization (MOPSO) algorithm is used to find nondominated solutions with four objectives: (i) maximize annual firm water supply; (ii) maximize annual firm energy production; (iii) minimize flood risk; and (iv) maximize ...

Appropriate renewable distributed generation (RDG) placement is one of the most significant issues for efficient operation current power systems. Since inverter-interfaced RDG lacks rotating mass to sustain system’s inertia, microgrids have low total system which impairs frequency stability and can yield voltage instability in severe disruptions. The virtual synchronous generator (VSG), uses co...

This paper presented a parallel hybrid electric vehicle (HEV) equipped with a hybrid energy storage system. To handle complex energy flow in the powertrain system of this HEV, a fuzzy-based energy management strategy was established. A chaotic multi-objective genetic algorithm, which optimizes the parameters of fuzzy membership functions, was also proposed to improve fuel economy and HC, CO, an...

One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed profile that requires the minimum energy consumption without degrading commercial running times or passenger comfort. When the trains are equipped with Automatic Train Operation systems (ATO) additional difficulties are involved. Their p...

Modern this paper proposes non dominated sorting genetic algorithm (NSGA-II) which has feature of adaptive crowding distance for finding optimal location and sizing of Static Var Compensators (SVC) in order to minimize real power losses and voltage deviation and also to improve voltage profile of a power system at the same time. While finding the optimal location and size of SVC, single line ou...

This paper presents an application of elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. This scheduling problem is a bi-objective problem considering two objectives. The first objective is minimization of makespan and the second one being the minimization of flowtime. As a multi-objectiv...

Evolutionary algorithms (EAs) have been systematically developed to solve mono-objective, multi-objective and many-objective optimization problems, in this order, over the past few decades. Despite some efforts in unifying different types of mono-objective evolutionary and non-evolutionary algorithms, there does not exist many studies to unify all three types of optimization problems together. ...