60 research outputs found
Linearized biogeography-based optimization with re-initialization and local search
Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses migration to share information among candidate solutions. One limitation of BBO is that it changes only one independent variable at a time in each candidate solution. In this paper, a linearized version of BBO, called LBBO, is proposed to reduce rotational variance. The proposed method is combined with periodic re-initialization and local search operators to obtain an algorithm for global optimization in a continuous search space. Experiments have been conducted on 45 benchmarks from the 2005 and 2011 Congress on Evolutionary Computation, and LBBO performance is compared with the results published in those conferences. The results show that LBBO provides competitive performance with state-of-the-art evolutionary algorithms. In particular, LBBO performs particularly well for certain types of multimodal problems, including high-dimensional real-world problems. Also, LBBO is insensitive to whether or not the solution lies on the search domain boundary, in a wide or narrow basin, and within or outside the initialization domain
Application of swarm mean-variance mapping optimization on location and tuning damping controllers
This paper introduces the use of the Swarm Variant of
the Mean-Variance Mapping Optimization (MVMO-S) to solving
the multi-scenario problem of the optimal placement and
coordinated tuning of power system damping controllers
(POCDCs). The proposed solution is tested using the classical
IEEE 39-bus test system, New England test system. This papers
includes performance comparisons with other emerging
metaheuristic optimization: comprehensive learning particle
swarm optimization (CLPSO), genetic algorithm with multi-parent
crossover (GA-MPC), differential evolution DE algorithm with
adaptive crossover operator, linearized biogeography-based
optimization with re-initialization (LBBO), and covariance matrix
adaptation evolution strategy (CMA-ES). Numerical results
illustrates the feasibility and effectiveness of the proposed
approach
Performance assessment of evolutionary algorithms in power system optimization problems
Due to the stochastic nature, there are several concerns on the effectiveness and robustness of evolutionary algorithms when applied to solve different kinds of optimization problems in power systems field. To address this issue, this paper provides a comparative analysis of several evolutionary algorithms based on parametric and non-parametric statistical tests. Numerical examples are based on hydrothermal system operation and transmission pricing optimization problems
Online estimation of equivalent model for cluster of induction generators: a MVMO-based approach
This paper presents an approach based on the hybrid variant of the mean-variance mapping optimization algorithm (MVMO-SH) for the estimation of an Equivalent Model for a
cluster of induction generators (IGs) from the on-line system response to a system frequency disturbance. Numerical results,
obtained by using a small-size test system, demonstrate the viewpoint and effectiveness of the proposed approach
Multi-objective operation optimization of an electrical distribution network with soft open point
With the increasing amount of distributed generation (DG) integrated into electrical distribution networks, various operational problems, such as excessive power losses, over-voltage and thermal overloading issues become gradually remarkable. Innovative approaches for power flow and voltage controls are required to ensure the power quality, as well as to accommodate large DG penetrations. Using power electronic devices is one of the approaches. In this paper, a multi-objective optimization framework was proposed to improve the operation of a distribution network with distributed generation and a soft open point (SOP). An SOP is a distribution-level power electronic device with the capability of real-time and accurate active and reactive power flow control. A novel optimization method that integrates a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and a local search technique – the Taxi-cab method, was proposed to determine the optimal set-points of the SOP, where power loss reduction, feeder load balancing and voltage profile improvement were taken as objectives. The local search technique is integrated to fine tune the non-dominated solutions obtained by the global search technique, overcoming the drawback of MOPSO in local optima trapping. Therefore, the search capability of the integrated method is enhanced compared to the conventional MOPSO algorithm. The proposed methodology was applied to a 69-bus distribution network. Results demonstrated that the integrated method effectively solves the multi-objective optimization problem, and obtains better and more diverse solutions than the conventional MOPSO method. With the DG penetration increasing from 0 to 200%, on average, an SOP reduces power losses by 58.4%, reduces the load balance index by 68.3% and reduces the voltage profile index by 62.1%, all compared to the case without an SOP. Comparisons between SOP and network reconfiguration showed the outperformance of SOP in operation optimization
Design of wind farm layout with non-uniform turbines using fitness difference based BBO
Biogeography-based optimization (BBO) is an emerging meta-heuristic algorithm. BBO is inspired from the migration of species from one island to another. This study presents the solution of the wind farm layout optimization problem with wind turbines having non-uniform hub heights and rotor radii using BBO and an improved version of BBO. This study proposes an improved version of BBO, Fitness Difference Based BBO (FD-BBO). FD-BBO is obtained by incorporating the concept of fitness differences in original BBO. First, in order to justify the superiority of FD-BBO over BBO, it is tested over standard test problems of optimization. The numerical results of FD-BBO are compared with the original version of BBO and an advanced version of BBO, Blended BBO (BBBO). Through graphical and statistical analyses, FD-BBO is established to be an efficient and accurate algorithm. The BBO, BBBO and FD-BBO are than applied to solve the wind farm layout optimization problem. In the considered problem, not only the location of the wind turbines but hub heights and rotor radii are also taken as decision variables. Two cases of the problems are dealt: turbines in the farm size of and turbines in the farm size of . Numerical results are compared with earlier published results and that of original BBO and Blended BBO. It is found that FD-BBO is the better approach to solving the problem under consideration
Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System
This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods
Multi-objective Anti-swing Trajectory Planning of Double-pendulum Tower Crane Operations using Opposition-based Evolutionary Algorithm
Underactuated tower crane lifting requires time-energy optimal trajectories
for the trolley/slew operations and reduction of the unactuated swings
resulting from the trolley/jib motion. In scenarios involving non-negligible
hook mass or long rig-cable, the hook-payload unit exhibits double-pendulum
behaviour, making the problem highly challenging. This article introduces an
offline multi-objective anti-swing trajectory planning module for a
Computer-Aided Lift Planning (CALP) system of autonomous double-pendulum tower
cranes, addressing all the transient state constraints. A set of auxiliary
outputs are selected by methodically analyzing the payload swing dynamics and
are used to prove the differential flatness property of the crane operations.
The flat outputs are parameterized via suitable B\'{e}zier curves to formulate
the multi-objective trajectory optimization problems in the flat output space.
A novel multi-objective evolutionary algorithm called Collective Oppositional
Generalized Differential Evolution 3 (CO-GDE3) is employed as the optimizer. To
obtain faster convergence and better consistency in getting a wide range of
good solutions, a new population initialization strategy is integrated into the
conventional GDE3. The computationally efficient initialization method
incorporates various concepts of computational opposition. Statistical
comparisons based on trolley and slew operations verify the superiority of
convergence and reliability of CO-GDE3 over the standard GDE3. Trolley and slew
operations of a collision-free lifting path computed via the path planner of
the CALP system are selected for a simulation study. The simulated trajectories
demonstrate that the proposed planner can produce time-energy optimal
solutions, keeping all the state variables within their respective limits and
restricting the hook and payload swings.Comment: 14 pages, 14 figures, 6 table
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