HC-PSOGWO: Hybrid Crossover Oriented PSO and GWO based Co-Evolution for Global optimization
07 Jun 2019-pp 162-167
TL;DR: The proposed HC-PSOGWO mainly focuses on better generalization, search procedure and diversification and is significantly better than other existing techniques.
Abstract: In order to introduce and maintain an optimum balance between exploration and exploitation in the search space, a hybrid crossover oriented Particle Swarm optimization(PSO) and Grey Wolf optimization(GWO) based co-evolution structure, abbreviated as HC-PSOGWO is proposed in this paper. The proposed HC-PSOGWO mainly focuses on better generalization, search procedure and diversification. Here both particles and wolves, as search agents, concurrently and independently explore the entire search space for optimal results. The learning procedure of GWO is also modified. Once, the exploration process is over, the evolved search agents from both the optimization techniques are crossed, they communicate among themselves, better agents are identified in the process and they are again allowed to modify the swarms targeted by the individual technique. The performance of the proposed technique is tested on 23 well known classical benchmark functions, one real world optimization problem and compared with several established optimization techniques. The experimental results and related analysis show that the proposed HC-PSOGWO is significantly better than other existing techniques.
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TL;DR: Swarm intelligence from natural to artificial systems, where people have search hundreds of times for their chosen books, but end up in malicious downloads instead of reading a good book with a cup of coffee in the afternoon.
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8 citations
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4 citations
References
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TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described
18,439 citations
"HC-PSOGWO: Hybrid Crossover Oriente..." refers methods in this paper
...So in this paper we choose to exploit the advantages of the hybridized procedure and use grey wolf optimization(GWO) [8] and particle swarm optimization(PSO) [3] as the integral components of the implementation....
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...(PSO) [3], firefly algorithm [4], artificial bee colony optimiza-...
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TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
10,082 citations
"HC-PSOGWO: Hybrid Crossover Oriente..." refers background or methods in this paper
...In the conventional GWO, the encircling procedure is described as follows [8]:...
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...other algorithms like GWO [8], PSO [19], FA [4], GSA [21], BA [22], DE [23] and TGWO [25] and to evaluate the overall exploitation and exploration property of HC-PSOGWO....
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...The GWO [8] algorithm is inspired from the basic hunting procedure and social hierarchy of the grey wolf in nature....
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...So in this paper we choose to exploit the advantages of the hybridized procedure and use grey wolf optimization(GWO) [8] and particle swarm optimization(PSO) [3] as the integral components of the implementation....
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...Grey wolf optimization (GWO) (S. Mirjalili et al., 2014) [8] is a population-based algorithm which utilizes the hunting mechanism of grey wolves....
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04 May 1998
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.
9,373 citations
"HC-PSOGWO: Hybrid Crossover Oriente..." refers background in this paper
...Inertia weight was further included to PSO to make the balance between exploitation and exploration [20]:...
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TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
7,090 citations
"HC-PSOGWO: Hybrid Crossover Oriente..." refers methods in this paper
...optimization [7] are the examples of few well-known metaheuristic optimization methods....
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TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
Abstract: Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.
6,377 citations
"HC-PSOGWO: Hybrid Crossover Oriente..." refers background in this paper
...tion (ABC) [5], spider monkey optimization (SMO) [6], whale...
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...All the algorithms of this category are inspired by various physical phenomena like food searching, hunting, bird flocking, animals’ behaviors, or evolutionary process, etc. Genetic algorithm (GA) [1], ant colony optimization (ACO) [2], particle swarm optimization (PSO) [3], firefly algorithm [4], artificial bee colony optimization (ABC) [5], spider monkey optimization (SMO) [6], whale optimization [7] are the examples of few well-known metaheuristic optimization methods....
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