Grey Wolf Optimizer

The Whale Optimization Algorithm

http://masou-keshtkar.persiangig.com/document/sdarticle.pdf

GSA: A Gravitational Search Algorithm

Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems. Just as the mathematics of biological genetics inspired the development of genetic algorithms (GAs), and the mathematics of biological neurons inspired the development of artificial neural networks, this paper considers the mathematics of biogeography as the basis for the development of a new field: biogeography-based optimization (BBO). We discuss natural biogeography and its mathematics, and then discuss how it can be used to solve optimization problems. We see that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO). This makes BBO applicable to many of the same types of problems that GAs and PSO are used for, namely, high-dimension problems with multiple local optima. However, BBO also has some features that are unique among biology-based optimization methods. We demonstrate the performance of BBO on a set of 14 standard benchmarks and compare it with seven other biology-based optimization algorithms. We also demonstrate BBO on a real-world sensor selection problem for aircraft engine health estimation.

/pdf/biogeography-based-optimization-50g2ml1vjr.pdf

Biogeography-Based Optimization

This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.

Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In the paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. The paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. The paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.

/pdf/evolutionary-programming-made-faster-1ts6nzmce0.pdf

Evolutionary programming made faster

This chapter discusses a number of recent results in evolutionary optimization. In particular, we show that the search step size of a variation operator plays a vital role in its efficient search of a landscape. We have derived the optimal search step size of mutation operators in evolutionary optimization. Based on this theoretical analysis, we have developed several new evolutionary algorithms which outperform existing evolutionary algorithms significantly on many benchmark functions.Most of the existing work in evolutionary optimization concentrates on different variation (i.e., search) operators, such as crossover and mutation. However, there may be a better way to solve a complex problem by transforming it into a simpler one first and then solving it. The key issue here is how to approximate the problem without changing the nature of the problem (i.e., the optima we wish to find). This chapter will present the latest results on landscape approximation and hybrid evolutionary algorithms.

Fast evolutionary algorithms

Crossover plays an important role in GA based search. There have been many empirical comparisons of different crossover operators in the literature. However, analytical results are limited. No theory has explained the behaviours of different crossover operators satisfactorily. The paper analyses crossover from quite a different point of view from the classical schema theorem. It explains the behaviours of different crossover operators through the investigation of crossover's search neighbourhood and search step size. It is shown that given the binary chromosome encoding scheme, GAs with a large search step size are better than GAs with a small step size for most problems. Since uniform crossover's search step size is larger than that of either one point or two point crossover, uniform crossover is expected to perform better than the other two. Similarly, two point crossover is expected to perform better than one point crossover due to its larger search step size. It is also shown that increasing the number of crossover points will increase crossover's search step size. The analytical results are supported by the experimental studies on 12 benchmark function optimisation problems.

Analysing crossover operators by search step size

In this paper, we introduce a new self-adaptive evolutionary algorithm for solving function optimization problems. The capabilities of the new algorithm include: a) self-adaptive choice of Gaussian or Cauchy mutation to balance the local and global search on the variable subspace, b) using multi-parent crossover to exchange global search information, c) using the best individual to take place the worst individual selection strategy to reduce the selection pressure and ensure to find a global optimization. These enhancements increase the capabilities of the algorithm to solve Shekel problems in a more robust and universal way. This paper will present some results of numerical experiments which show that the new algorithm is more robust and universal than its competitors.

A self-adaptive mutations with multi-parent crossover evolutionary algorithm for solving function optimization problems

In this paper we describe an implementation of some kinds of parallel genetic algorithms on the PVM. Parallel Virtual Machine, a portable parallel environment. We give details of a genetic algorithm running on many small subpopulations with an occasional identification and exchange of their useful information among subpopulations by means of message-passing functions of PVM. In this work, experiments were done to compare the parallel genetic algorithm and traditional sequential genetic algorithms.

Guangming Lin

Papers

Evolutionary programming made faster

Fast evolutionary algorithms

Analysing crossover operators by search step size

A self-adaptive mutations with multi-parent crossover evolutionary algorithm for solving function optimization problems

Parallel genetic algorithm on PVM