10,218 research outputs found

    Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition

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    Multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to implement, Particle Swarm Optimization (PSO) has become one of the most popular single-objective optimizers for continuous problems, and recently it has been successfully extended to the multi-objective domain. However, no investigation on the application of PSO within a multi-objective decomposition framework exists in the context of combinatorial optimization. This is precisely the focus of the paper. More specifically, we study the incorporation of Geometric Particle Swarm Optimization (GPSO), a discrete generalization of PSO that has proven successful on a number of single-objective combinatorial problems, into a decomposition approach. We conduct experiments on manyobjective 1/0 knapsack problems i.e. problems with more than three objectives functions, substantially harder than multi-objective problems with fewer objectives. The results indicate that the proposed multi-objective GPSO based on decomposition is able to outperform two version of the wellknow MOEA based on decomposition (MOEA/D) and the most recent version of the non-dominated sorting genetic algorithm (NSGA-III), which are state-of-the-art multi-objective evolutionary approaches based on decomposition

    Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

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    As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

    Performance evaluation and optimization of swarms of robots in a specific task

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    Objectives and methodology: Nowadays the swarms of robots represent an alternative to solve a wide range of tasks as search, aggregation, predatorprey, foraging, etc. However, determining how well the task is resolved is an important current problem, assign evaluation metrics to tasks performed by swarms of robots is very useful in order to measure the performance of a particular swarm in the task resolution. Find the control parameters of a swarm of robots that resolves a task with the best possible performance represents many benefits as saving of energetic resources and time. The general objective in this thesis is to evaluate and improve the performance of a swarm of robots in the resolution of a particular task, for that reason the following specific objectives are proposed: 1) To describe a flocking task with target zone search and to determine evaluation metrics that measure the task resolution; 2) To implement behavior policies for a simulated swarm of quadrotors; 3) To implement multi-objective optimization techniques in order to find the best sets of control parameters of the swarm that resolve the proposed task with the best possible performance; 4) To compare the performance of the implemented multi-objective optimization algorithms in order to determine which algorithm represents the best option to optimize this type of tasks. Different methods to control swarms of robots have been proposed, in this thesis a bio-inspired model based in repulsion (∆r), orientation (∆o) and attraction (∆a) tendencies between biological species as bird flocks and schools of fish is applied in the simulated swarm of quadrotors. Different experiments are proposed, the flocking task with target zone search is optimized for swarms of quadrotors of 5, 10 and 20 members and with two different conditions in the environment, one case without obstacles and another case with obstacles in the arena. The task is evaluated by four proposed objective functions formulated as minimization problems which are oriented to reach four main objectives in the task, as these objectives functions are minimized the desired behavior of the swarm of quadrotors is reached. The Multi-Objective Particle Swarm Optimization (MOPSO), the Nondominated Sorting Genetic Algorithm II using Differential Evolution (NSGA-II-DE) and the Multiobjective Evolutionary Algorithm based on Decomposition using Differential Evolution (MOEA/D-DE) are used to optimize the control parameters ∆r, ∆o and ∆a for the proposed task in each experiment. The Hypervolume measure (HV ), a modified C-metric (Q) and the time per cycle (T P C) are the selected metrics to evaluate the performance of the multi-objective optimization algorithms. Contributions and conclusions: The obtained results show that the selected behavior policies produces collaborative interactions between members of the swarm that benefit the resolution of the task. Use multi-objective optimization techniques directly on the quadrotor swarm simulator produces small number of optimized solutions because the optimization process is only suitable with small populations and with a reduced number of cycles due to the..

    An evolutionary algorithm with double-level archives for multiobjective optimization

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    Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problemlevel and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed

    Improved dynamical particle swarm optimization method for structural dynamics

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    A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version

    Multi-objective volleyball premier league algorithm

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    This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multiobjective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems

    State-of-the-art in aerodynamic shape optimisation methods

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    Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
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