On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

Draa, Amer

doi:10.1016/j.asoc.2015.05.015

Cited by 107 publications

(31 citation statements)

References 67 publications

(149 reference statements)

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“…Liao et al (2013) showed that the claimed superiority of the newer variants of artificial bee colony (ABC) algorithms over the older methods may be confirmed when tests are performed on simple low-dimensional problems, but not when more difficult high-dimensional problems are considered. Draa (2015) showed, based on a wide selection of problems, that the performance of the flower pollination algorithm, one of the recently introduced approaches, turns out to be relatively poor when compared with classical algorithms, in contradiction to what is claimed in the literature. Similarly, Fong et al (2016) found the lack of superiority of a novel bat-inspired algorithm over the classical PSO.…”

Section: Background: Recent Criticisms Of Optimization Metaheuristicsmentioning

confidence: 51%

Searching for structural bias in particle swarm optimization and differential evolution algorithms

Piotrowski

Napiorkowski

2016

Swarm Intell

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During the last two decades, a large number of metaheuristics have been proposed, leading to various studies that call for a deeper insight into the behaviour, efficiency and effectiveness of such methods. Among numerous concerns that are briefly reviewed in this paper, the presence of a structural bias (i.e. the tendency, not justified by the fitness landscape, to visit some regions of the search space more frequently than other regions) has recently been detected in simple versions of the genetic algorithm and particle swarm optimization. As of today, it remains unclear how frequently such a behaviour occurs in population-based swarm intelligence and evolutionary computation methods, and to what extent structural bias affects their performance. The present study focuses on the search for structural bias in various variants of particle swarm optimization and differential evolution algorithms, as well as in the traditional direct search methods proposed by Nelder-Mead and Rosenbrock half a century ago. We found that these historical direct search methods are structurally unbiased. However, most tested new metaheuristics are structurally biased, and at least some presence of structural bias can be observed in almost all their variants. The presence of structural bias seems to be stronger in particle swarm optimization algorithms than in differential evolution algorithms. The relationships between the strength of the structural bias and the dimensionality of the search space, the number of allowed function calls and the population size are complex and hard to generalize. For 14 algorithms tested on the CEC2011 realworld problems and the CEC2014 artificial benchmarks, no clear relationship between the strength of the structural bias and the performance of the algorithm was found. However, at least for artificial benchmarks, such old and structurally unbiased methods like Nelder-Mead algorithm performed relatively well. This is a warning that the presence of structural bias in novel metaheuristics may hamper their search abilities.

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Section: Background: Recent Criticisms Of Optimization Metaheuristicsmentioning

confidence: 51%

Searching for structural bias in particle swarm optimization and differential evolution algorithms

Piotrowski

Napiorkowski

2016

Swarm Intell

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“…Furthermore, based on those results, the Friedman test [51][52][53], which is a non-parametric alternative to ANOVA, is conducted with SPSS 22 to check whether the StGA, the IMGA, the RTS, the DPGA, and GameEA have similar performances. The null hypothesis is set to: "the distributions of StGA, IMGA, RTS, DPGA, and GameEA are the same".…”

Section: Results and Comparision Analysismentioning

confidence: 99%

Game Theory-Inspired Evolutionary Algorithm for Global Optimization

Yang

2017

Algorithms

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Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields. This paper introduces a game theory-inspired evolutionary algorithm for global optimization (GameEA). A formulation to estimate payoff expectations is provided, which is a mechanism to make a player become a rational decision-maker. GameEA has one population (i.e., set of players) and generates new offspring only through an imitation operator and a belief-learning operator. An imitation operator adopts learning strategies and actions from other players to improve its competitiveness and applies these strategies to future games where one player updates its chromosome by strategically copying segments of gene sequences from a competitor. Belief learning refers to models in which a player adjusts his/her strategies, behavior or chromosomes by analyzing the current history information to improve solution quality. Experimental results on various classes of problems show that GameEA outperforms the other four algorithms on stability, robustness, and accuracy.

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“…We do not compare the performance in terms of execution times as most implementations are not available to be executed on the computer we used (a desktop PC with Windows 8, 2.5 GHz i5 CPU, 8 GB of RAM). Furthermore, in the case of meta-heuristic-based strategies, the time execution comparison can also be unfair [69][70][71][72] as the number of fitness evaluation varies significantly for each strategies.…”

Section: Benchmarking With Existing Strategies On Test Sizesmentioning

confidence: 99%

A Tabu Search hyper-heuristic strategy for t-way test suite generation

Zamli

Alkazemi

Kendall

2016

Applied Soft Computing

101

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This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning Based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks.

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On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

Cited by 107 publications

References 67 publications

Searching for structural bias in particle swarm optimization and differential evolution algorithms

Searching for structural bias in particle swarm optimization and differential evolution algorithms

Game Theory-Inspired Evolutionary Algorithm for Global Optimization

A Tabu Search hyper-heuristic strategy for t-way test suite generation

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