The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Comparison of Particle Swarm Optimization and the Genetic Algorithm
Abstract
Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. PSO is similar to the Genetic Algorithm (GA) in the sense that these two evolutionary heuristics are population-based search methods. In other words, PSO and the GA move from a set of points (population) to another set of points in a single iteration with likely improvement using a combination of deterministic and probabilistic rules. The GA and its many versions have been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly nonlinear, mixed integer optimization problems that are typical of complex engineering systems. The drawback of the GA is its expensive computational cost. This paper attempts to examine the claim that PSO has the same effectiveness (finding the true global optimal solution) as the GA but with significantly better computational efficiency (less function evaluations) by implementing statistical analysis and formal hypothesis testing. The performance comparison of the GA and PSO is implemented using a set of benchmark test problems as well as two space systems design optimization problems, namely, telescope array configuration and spacecraft reliability-based design.
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Self-citationA novel principle for optimization of the instantaneous Fourier plane coverage of correction arrays
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Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey
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Metadata
- Authors
- Hassan, R., Cohanim, B., de Weck, O., & Venter, G.
- Venue
- 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
- Published
- 2005-04-18
- OpenAlex
- W2320438893
- Cited by
- 1,198
- References
- 10
- FWCI
- 11.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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