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: The proposed EM‐MOPSO approach is first tested for few test problems taken from the literature and evaluated with standard performance measures, and shows that the proposed approach is a viable alternative to solve multi‐objective water resources and hydrology problems.
Abstract: A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist-mutation (EM) operator are introduced. The proposed EM-MOPSO approach is first tested for few test problems taken from the literature and evaluated with standard performance measures. It is found that the EM-MOPSO yields efficient solutions in terms of giving a wide spread of solutions with good convergence to true Pareto optimal solutions. On achieving good results for test cases, the approach was applied to a case study of multi-objective reservoir operation problem, namely the Bhadra reservoir system in India. The solutions of EM-MOPSOs yield a trade-off curve/surface, identifying a set of alternatives that define optimal solutions to the problem. Finally, to facilitate easy implementation for the reservoir operator, a simple but effective decision-making approach was presented. The results obtained show that the proposed approach is a viable alternative to solve multi-objective water resources and hydrology problems.
183 citations
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TL;DR: A novel particle swarm optimization algorithm based on Hill function is presented to minimize makespan and energy consumption in dynamic flexible flow shop scheduling problems and shows that the proposed algorithm outperforms the behavior of state of the art algorithms.
183 citations
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TL;DR: A new method, named the multigrouped particle swarm optimization (MGPSO), keeps basic concepts of the PSO, and shows a more straightforward convergence compared to conventional hybrid type approaches.
Abstract: In this paper, a new algorithm for the multimodal function optimization is proposed, based on the particle swarm optimization (PSO). A new method, named the multigrouped particle swarm optimization (MGPSO), keeps basic concepts of the PSO, and, thus, shows a more straightforward convergence compared to conventional hybrid type approaches. Moreover, the MGPSO has a unique advantage in that one can search N superior peaks of a multimodal function when the number of groups is N. The usefulness of the proposed algorithm was verified by the application to various case studies, including a practical electromagnetic optimization problem
183 citations
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TL;DR: Swarm intelligence as mentioned in this paper is a meta-heuristic approach to solving a variety of problems in which the collective behavior of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns.
Abstract: Nature-inspired intelligent swarm technologies deals with complex problems that might be impossible to solve using traditional technologies and approaches. Swarm intelligence techniques (note the difference from intelligent swarms) are population-based stochastic methods used in combinatorial optimization problems in which the collective behavior of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. Swarm intelligence represents a meta heuristic approach to solving a variety of problems
183 citations
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TL;DR: Compared with the widely used differential evolution and particle swarm optimization, SADEA can obtain comparable results, but achieves a 3 to 7 times speed enhancement for antenna design optimization.
Abstract: In recent years, various methods from the evolutionary computation (EC) field have been applied to electromagnetic (EM) design problems and have shown promising results However, due to the high computational cost of the EM simulations, the efficiency of directly using evolutionary algorithms is often very low (eg, several weeks' optimization time), which limits the application of these methods for many industrial applications To address this problem, a new method, called surrogate model assisted differential evolution for antenna synthesis (SADEA), is presented in this paper The key ideas are: (1) A Gaussian Process (GP) surrogate model is constructed on-line to predict the performances of the candidate designs, saving a lot of computationally expensive EM simulations (2) A novel surrogate model-aware evolutionary search mechanism is proposed, directing effective global search even when a traditional high-quality surrogate model is not available Three complex antennas and two mathematical benchmark problems are selected as examples Compared with the widely used differential evolution and particle swarm optimization, SADEA can obtain comparable results, but achieves a 3 to 7 times speed enhancement for antenna design optimization
183 citations