A survey on optimization metaheuristics

Ilhem Boussaïd, Julien Lepagnot, Patrick Siarry
2013 Information Sciences  
Metaheuristics are widely recognized as efficient approache s for many hard optimization problems. This paper provides a survey of some of the main metaheuristics. It outlines the components and concepts that are used in various metaheuristics in order to analyze their similarities and differences. The classification adopted in this paper differentiates between single solution based metaheuristics and population based metaheuristics . The literature survey is accompanied by the presentation of
more » ... eferences for further details, including applications. Recent trends are also briefly discussed. (P. Siarry). Information Sciences 237 (2013) Contents lists available at SciVer se ScienceD irect Infor mation Sciences j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i n s was proposed by Walker et al. [277] . Another significant progress is the developmen t of the particle swarm optimization by Kennedy and Eberhart in 1995 [145]. The same year, Hansen and Ostermeier proposed CMA-ES [121] . In 1996, Mühlenbein and Paaß proposed the estimation of distribution algorithm [190] . In 1997, Storn and Price proposed differential evolution [253] . In 2002, Passino introduced an optimizati on algorithm based on bacterial foraging [200] . Then, Simon proposed a biogeography-b ased optimization algorithm in 2008 [247] . The considerable development of metaheurist ics can be explained by the significant increase in the processing power of the computer s, and by the developmen t of massively parallel architectur es. These hardware improvem ents relativize the CPU time-costly nature of metaheu ristics. A metaheurist ic will be successful on a given optimizati on problem if it can provide a balance between the exploration (diversification) and the exploitati on (intensification). Exploitation is needed to identify parts of the search space with high quality solutions. Exploitati on is important to intensify the search in some promising areas of the accumulated search experience. The main differences between the existing metaheuristics concern the particular way in which they try to achieve this balance [28] . Many classification criteria may be used for metaheurist ics. This may be illustrate d by considering the classification of metaheuristics in terms of their features with respect to different aspects concerning the search path they follow, the use of memory, the kind of neighborho od exploration used or the number of current solutions carried from one iteration to the next. For a more formal classification of metaheuristics we refer the reader to [28, 258] . The metaheurist ic classification, which differentiate s between Single-Solution Based Metaheur istics and Population-Bas ed Metaheuristics , is often taken to be a fundamenta l distinctio n in the literature. Roughly speaking, basic single-solut ion based metaheuristics are more exploitation oriented whereas basic population-bas ed metaheu ristics are more exploration oriented. The purpose of this paper is to present a global overview of the main metaheuristics and their principles. That attempt of survey on metaheu ristics is structured in the following way. Section 2 shortly presents the class of single-so lution based metaheurist ics, and the main algorithms that belong to this class, i.e. the simulated annealing method, the tabu search, the GRASP method, the variable neighborhood search, the guided local search, the iterated local search, and their variants. Section 3 describes the class of metaheurist ics related to population-based metaheurist ics, which manipulate a collection of solutions rather than a single solution at each stage. Section 3.1 describes the field of evolutionary computation and outlines the common search components of this family of algorithms (e.g., selection, variation, and replacemen t). In this subsection, the focus is on evolutionary algorithms such as genetic algorithms, evolution strategie s, evolutionary programming, and genetic programmin g. Section 3.2 presents other evolutionar y algorithms such as estimation of distribution algorithms, differential evolution, coevolution ary algorithms, cultural algorithms and the scatter search and path relinking. Section 3.3 contains an overview of a family of nature inspired algorithms related to Swarm Intelligence. The main algorithms belonging to this field are ant colonies, particle swarm optimization, bacterial foraging, bee colonies, artificial immune systems and biogeography-b ased optimization. Finally, a discussion on the current research status and most promising paths of future research is presented in Section 4. Single-solutio n based metaheuristi cs In this section, we outline single-solut ion based metaheuristics, also called trajectory methods . Unlike population-bas ed metaheurist ics, they start with a single initial solution and move away from it, describing a trajector y in the search space. Some of them can be seen as "intelligent" extensions of local search algorithms. Trajectory methods mainly encompass the simulated annealing method, the tabu search, the GRASP method, the variable neighborho od search, the guided local search, the iterated local search, and their variants.
doi:10.1016/j.ins.2013.02.041 fatcat:osz7frllkrfqfke4crydwr4pe4