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Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.


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TL;DR: A selected list of test problems for unconstrained optimization, using at least a subset of functions with diverse properties to make sure whether or not the tested algorithm can solve certain type of optimization efficiently.
Abstract: Test functions are important to validate new optimization algorithms and to compare the performance of various algorithms There are many test functions in the literature, but there is no standard list or set of test functions one has to follow New optimization algorithms should be tested using at least a subset of functions with diverse properties so as to make sure whether or not the tested algorithm can solve certain type of optimization efficiently Here we provide a selected list of test problems for unconstrained optimization

123 citations

Journal ArticleDOI
TL;DR: In this article, the authors use evolutionary computation to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics, such as particle swarm optimization, differential evolution, and covariance matrix adaptation-evolution strategy (CMA-ES).
Abstract: We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular, we analyze particle swarm optimization (PSO), differential evolution (DE), and covariance matrix adaptation-evolution strategy (CMA-ES). Each evolutionary algorithm is contrasted with the others and with a robust nonstochastic gradient follower (i.e., a hill climber) based on Newton-Raphson. The evolved benchmark problems yield insights into the operation of PSOs, illustrate benefits and drawbacks of different population sizes, velocity limits, and constriction (friction) coefficients. The fitness landscapes made by genetic programming reveal new swarm phenomena, such as deception, thereby explaining how they work and allowing us to devise better extended particle swarm systems. The method could be applied to any type of optimizer.

123 citations

Journal ArticleDOI
TL;DR: In this article, a systematic approach for determination of optimal mix of resources is presented for an autonomous hybrid power system, which comprises of diesel, photovoltaic, wind and battery storage.

123 citations

Journal ArticleDOI
01 Mar 2011
TL;DR: The proposed NAPSO algorithm is validated on test systems consisting of 6, 10, 15, 40 and 80 generators with the objective functions possessing prohibited zones, multi-fuel effects and valve-point loading effects.
Abstract: Economic dispatch (ED) problem is a nonlinear and non-smooth optimization problem when valve-point effects, multi-fuel effects and prohibited operating zones (POZs) have been considered. This paper presents an efficient evolutionary method for a constrained ED problem using the new adaptive particle swarm optimization (NAPSO) algorithm. The original PSO has difficulties in premature convergence, performance and the diversity loss in optimization process as well as appropriate tuning of its parameters. In the proposed algorithm, to improve the global searching capability and prevent the convergence to local minima, a new mutation is integrated with adaptive particle swarm optimization (APSO). In APSO, the inertia weight is tuned by using fuzzy IF/THEN rules and the cognitive and the social parameters are self-adaptively adjusted. The proposed NAPSO algorithm is validated on test systems consisting of 6, 10, 15, 40 and 80 generators with the objective functions possessing prohibited zones, multi-fuel effects and valve-point loading effects. The research results reveal the effectiveness and applicability of the proposed algorithm to the practical ED problem.

123 citations

Journal ArticleDOI
TL;DR: Experimental results show that CenterPSO achieves better performance than LDWPSO, and the two algorithms are extensively compared on three well-known benchmark functions with 10, 20, 30 dimensions.

123 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023183
2022471
202110
20207
201926
2018171