Shuffled Complex Evolution 2
Shuffled Complex Evolution ¾An Evolutionary algorithm That performs local and global arch
¾ A solution evolves locally through a memetic evolution (Local arch)
¾This local arch is similar to that performed by the particle swarm optimization
¾Then, it evolves globally by changing of information from parallel local arches (Global arch)¾This is also called or similar to what is called “Shuffled frog leaping algorithm”
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¾ A solution to a given problem is reprented in the form of a string consisting of a t of elements (memes) that hold a t of values for the optimization variables ¾The fitness of each individual is determined by evaluating it against an objective function
1 2
3. . . . .. . . . . . . . . . . . N Variable value Number of variables
781852261
. . . .9736304434
¾Each individual reprents a feasible solution for the problem under study
¾The length of the chromosome equals the number of variables
¾The solution of a given problem started by creating population at random
¾
Calculating the fitness of each individual ¾Then, Starting the evolutionary process
Shuffled Complex Evolution
5¾The whole population is divided into a t of memeplexes (subts of the whole domain)
风湿性疾病¾
Each subt reprents a local area of the whole domain ¾ A local arch is performed at each memeplex through a memetic evolution
遥控器¾After a number of memetic evolution steps, information is pasd among memeplexes through a shuffling process ¾The local arch and the shuffling process continue until convergence criteria are satisfied Evolutionary Process
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¾So, shuffled complex evolution tries to balance between a wide-scan of a large solution space and deep arch of promising locations
¾It depends mainly on partitioning the solution space into local communities and perform local arch within the communities
¾Then, it shuffles the local communities to perform global arch
Shuffled Complex Evolution Evolutionary Process
Evolutionary Process
¾So, shuffled complex evolution tries to balance between a wide-scan of a large solution space and deep arch of
promising locations
¾It depends mainly on partitioning the solution space into local communities and perform local arch within the座头市物语
脱臼的处理方法
communities
¾Then, it shuffles the local communities to perform global arch
党日活动主题7
Shuffled Complex Evolution
Evolutionary Process
门铃声
¾The population is sorted in a descending order according to their fitness
¾Then, the entire population is divided into m memeplexes, each containing n individuals (i.e., P = m x n)
¾To ensure that each memeplex reprents the whole solution space, in this process, the first indivi
dual goes to
the first memeplex, the cond goes to the cond
memeplex, individual m goes to the m th memeplex, and
individual m + 1 goes to the first memeplex, and so on
8姜的种植方法
9
¾Within each memeplex, the individuals with the best and the worst fitness are identified as X b and X w , respectively ¾Also, the individual with the global best fitness is identified as X g
¾Then, an evolution process is applied to improve the individual with the worst fitness in each cycle ¾SCE applies memetic evolution (cultural) rather than genetic (biological) evolution
五花肉烧芋头Evolutionary Process 10¾Accordingly, the position of the individual with the worst fitness is adjusted to improve its position through exchanging information with the best solution Change in position (D i )
= rand() . (X b –X w )New position X w = current position X w + D i Shuffled Complex Evolution Evolutionary Process
¾If this process produces a better solution, it replaces the worst. Otherwi, the calculations in Eqs. 1 and 2 are repeated with respect to the global best (X g replaces X b )
