Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization
TL;DR: Experimental results in terms of the likelihood of convergence to a global optimal solution and the solution speed suggest that the SFLA can be an effective tool for solving combinatorial optimization problems.
Abstract: A memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) has been developed for solving combinatorial optimization problems. The SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The SFLA consists of a set of interacting virtual population of frogs partitioned into different memeplexes. The virtual frogs act as hosts or carriers of memes where a meme is a unit of cultural evolution. The algorithm performs simultaneously an independent local search in each memeplex. The local search is completed using a particle swarm optimization-like method adapted for discrete problems but emphasizing a local search. To ensure global exploration, the virtual frogs are periodically shuffled and reorganized into new memplexes in a technique similar to that used in the shuffled complex evolution algorithm. In addition, to provide the opportunity for random generation of improved information,...
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TL;DR: A survey of metaheuristic research in literature consisting of 1222 publications from year 1983 to 2016 is performed to highlight potential open questions and critical issues raised in literature and provides guidance for future research to be conducted more meaningfully.
Abstract: Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.
467 citations
TL;DR: An up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering and key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
Abstract: The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
457 citations
TL;DR: A comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate, and the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Abstract: Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.
454 citations
Cites methods from "Shuffled frog-leaping algorithm: a ..."
...Other algorithms such as shuffled frog-leaping algorithm and particle swarm optimizers have been applied to various optimization problems (Eusuff et al. 2006; He et al. 2004; Huang 1996)....
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TL;DR: In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate.
Abstract: Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.
425 citations
TL;DR: This review identifies the popularly used algorithms within the domain of bio-inspired algorithms and discusses their principles, developments and scope of application, which would pave the path for future studies to choose algorithms based on fitment.
Abstract: Review of applications of algorithms in bio-inspired computing.Brief description of algorithms without mathematical notations.Brief description of scope of applications of the algorithms.Identification of algorithms whose applications may be explored.Identification of algorithms on which theory development may be explored. With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible. It is increasingly being recognised that applications of intelligent bio-inspired algorithms are necessary for addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. More and more such intelligent algorithms are thus being explored for solving different complex problems. While some studies are exploring the application of these algorithms in a novel context, other studies are incrementally improving the algorithm itself. However, the fast growth in the domain makes researchers unaware of the progresses across different approaches and hence awareness across algorithms is increasingly reducing, due to which the literature on bio-inspired computing is skewed towards few algorithms only (like neural networks, genetic algorithms, particle swarm and ant colony optimization). To address this concern, we identify the popularly used algorithms within the domain of bio-inspired algorithms and discuss their principles, developments and scope of application. Specifically, we have discussed the neural networks, genetic algorithm, particle swarm, ant colony optimization, artificial bee colony, bacterial foraging, cuckoo search, firefly, leaping frog, bat algorithm, flower pollination and artificial plant optimization algorithm. Further objectives which could be addressed by these twelve algorithms have also be identified and discussed. This review would pave the path for future studies to choose algorithms based on fitment. We have also identified other bio-inspired algorithms, where there are a lot of scope in theory development and applications, due to the absence of significant literature.
397 citations
Cites background or methods from "Shuffled frog-leaping algorithm: a ..."
...The extension of shuffled frog-leaping algorithm (Eusuff et al., 2006) combines the benefits of genetic-based memetic algorithm and the social behaviour-based swarm optimization algorithms....
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...As per literature (Eusuff et al., 2006; Li et al., 2012), the quality of outcome of the algorithm rises with the increase in the frog count in the population as well as the frog count in a sub-complex, but at the cost of number of function evaluation required to find the solution, which affects…...
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References
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Book•
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
32,573 citations
"Shuffled frog-leaping algorithm: a ..." refers background or methods in this paper
...The population is partitioned into several communities (complexes), each of which is permitted to evolve independently....
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...3.1 Shuffled complex evolution algorithm The SCE algorithm (Duan et al. 1992) combines the ideas of controlled random search (CRS) algorithms (Price 1978, 1983, 1987) with the concepts of competitive evolution (Holland 1975) and shuffling....
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...Genetic algorithms (GAs), introduced by Holland (1975), are inspired by the genetic mechanisms of natural species evolution....
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TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Abstract: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point. The simplex adapts itself to the local landscape, and contracts on to the final minimum. The method is shown to be effective and computationally compact. A procedure is given for the estimation of the Hessian matrix in the neighbourhood of the minimum, needed in statistical estimation problems.
27,271 citations
"Shuffled frog-leaping algorithm: a ..." refers methods in this paper
...(iv) The simplex algorithm (Nelder and Mead 1965), a direct search method, is used to generate the offspring, i.e. to evolve the subcomplexes....
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...(iv) The simplex algorithm (Nelder and Mead 1965 ), a direct search method, is used to generate the offspring, i....
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04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
14,477 citations
"Shuffled frog-leaping algorithm: a ..." refers methods in this paper
...PSO was originally designed and developed by Eberhart and Kennedy (1995) and it was extended by Eberhart et al. (1996) and Kennedy (1997)....
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...PSO was originally designed and developed by Eberhart and Kennedy (1995) and it was extended by Eberhart et al....
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01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
11,224 citations
Book•
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TL;DR: In this paper, the authors take up the concepts of altruistic and selfish behaviour; the genetical definition of selfish interest; the evolution of aggressive behaviour; kinship theory; sex ratio theory; reciprocal altruism; deceit; and the natural selection of sex differences.
Abstract: Science need not be dull and bogged down by jargon, as Richard Dawkins proves in this entertaining look at evolution. The themes he takes up are the concepts of altruistic and selfish behaviour; the genetical definition of selfish interest; the evolution of aggressive behaviour; kinship theory; sex ratio theory; reciprocal altruism; deceit; and the natural selection of sex differences. Readership: general; students of biology, zoology, animal behaviour, psychology.
10,880 citations