Topic
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 new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization is presented and shows itself as a general purpose tool able to effectively adapt itself to different electromagnetic optimization problems.
Abstract: A new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) is presented. This is a hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the particle swarm optimization (PSO) and genetic algorithms (GAs). The algorithm effectiveness has been tested here with respect to both its "ancestors," GA and PSO, dealing with an electromagnetic application, the optimization of a linear array. The here proposed method shows itself as a general purpose tool able to effectively adapt itself to different electromagnetic optimization problems
115 citations
01 Jan 1984
TL;DR: This dissertation addresses the topic of portable and machine-independent program optimization on a standard, well-defined intermediate code and confirms the advantages of using portable machine- independent optimization in a retargetable compiler system.
Abstract: This dissertation addresses the topic of portable and machine-independent program optimization on a standard, well-defined intermediate code. The design of the intermediate code, and features needed to support machine-independent optimization are discussed. A number of new techniques in program optimization are presented. These techniques when applied can substantially reduce both the implementation complexities and running time of optimizers in general, with no sacrifice in the optimizations performed. A register allocation algorithm based on node coloring, suitable for use in the machine-independent context, is introduced. An implementation of these techniques in the machine-independent optimizer UOPT is presented. The optimization performance, efficiency and the relative importance among the different types of optimization transformations are studied according to timing measurements, optimization statistics and by variation in optimization parameters. Finally, the effectiveness of portable machine-independent optimization on a number of target machines that support the intermediate code is discussed, based on optimization performance data in the different machines and comparisons of machine characteristics. The overall evaluation confirms the advantages of using portable machine-independent optimization in a retargetable compiler system.
115 citations
TL;DR: A multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic grid-connected systems (PVGCSs) is presented, to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized.
115 citations
18 May 2009
TL;DR: A clustering particle swarm optimizer (CPSO) for dynamic optimization problems using hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy and a fast local search method to find the near optimal solutions in a local promising region in the search space.
Abstract: In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.
115 citations