A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems
15 Apr 2016-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 140, Iss: 12, pp 10-23
TL;DR: The experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed algorithm is competitive to those achieved by the standard flower pollination algorithm.
Abstract: pollination algorithm (FP) is a new nature-inspired algorithm, based on the characteristics of flowering plants. Combining with the features of flower pollination algorithm, an improved simulated annealing algorithm is proposed in this paper (FPSA). It can improve the speed of annealing. The initial state of simulated annealing and new solutions are generated by flower pollination. Therefore, it has the advantage of high quality and efficiency. The method combines the standard flower pollination algorithm (FP) with simulated annealing to enhance the search performance and speeds up the global convergence rate. Structural engineering optimization problems are presented to demonstrate the effectiveness and robustness of the proposed algorithm. The experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed algorithm is competitive to those achieved by the
Citations
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TL;DR: This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications, and a comparison between FPA and six different metaheuristics on solving a constrained engineering optimization problem.
Abstract: Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem
. The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem.
139 citations
Cites background or methods from "A Hybrid Flower Pollination Algorit..."
...Also, Abdel-Baset and Hezam (2016) combined FPA and SA, but in a differentmanner....
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...In Ramadas and Kumar (2016), Abdel-Baset and Hezam (2015) and Abdel-Baset and Hezam (2016), DE, GA, and simulated annealing (SA) (Tahani et al. 2015; Kirkpatrick et al. 1983) respectively were employed sequentially to adjust the solution obtained by FPA....
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01 Jan 2018
TL;DR: This work intends to analyze nature-inspired algorithms both qualitatively and quantitatively, and briefly outline the links between self-organization and algorithms, and then analyze algorithms using Markov chain theory, dynamic system and other methods.
Abstract: Nature-inspired algorithms are a class of effective tools for solving optimization problems and these algorithms have good properties such as simplicity, flexibility and high efficiency. Despite their popularity in practice, a mathematical framework is yet to be developed to analyze these algorithms theoretically. This work intends to analyze nature-inspired algorithms both qualitatively and quantitatively. We briefly outline the links between self-organization and algorithms, and then analyze algorithms using Markov chain theory, dynamic system and other methods. This can serve as a basis for building a multidisciplinary framework for algorithm analysis.
98 citations
01 Jan 2018
TL;DR: This chapter provides a comprehensive review for FPA variants from 2012 to present, which has seen many variants of FPA developed by modification, hybridization, and parameter-tuning to cope with the complex nature of optimization problems.
Abstract: The flower pollination algorithm (FPA) is a nature-inspired algorithm that imitates the pollination behavior of flowering plants. Optimal plant reproduction strategy involves the survival of the fittest as well as the optimal reproduction of plants in terms of numbers. These factors represent the fundamentals of the FPA and are optimization-oriented. Yang developed the FPA in 2012, which has since shown superiority to other metaheuristic algorithms in solving various real-world problems, such as power and energy, signal and image processing, communications, structural design, clustering and feature selection, global function optimization, computer gaming, and wireless sensor networking. Recently, many variants of FPA have been developed by modification, hybridization, and parameter-tuning to cope with the complex nature of optimization problems. Therefore, this chapter provides a comprehensive review for FPA variants from 2012 to present.
92 citations
TL;DR: The comparative study, statistical analysis, and the findings suggest that the proposed AEFA-C is an efficient constrained optimizer.
Abstract: Nature-inspired optimization algorithms have attracted significant attention from researchers during the past decades due to their applicability to solving the challenging optimization problems, efficiently. Many intelligent systems require an excellent constrained optimization scheme to act as an artificially intelligent system. Artificial electric field algorithm (AEFA) is an intelligently designed artificial system that deals with the purpose of function optimization. AEFA works on the principle of Coulombs’ law of electrostatic force and Newtons’ law of motion. The present article extends the AEFA algorithm for constrained optimization problems by introducing the new velocity and position bound strategies. These bounds lead the particle to interact with each other within the domain of the problem, and they are allowed to learn from the problem space individually. They also help to make a better balance between exploration and exploitation by controlling the position update of the particles. The challenging IEEE CEC 2017 constrained benchmark set of 28 problems, and five multidimensional non-linear structural design optimization problems are solved using AEFA-C, which tests the effectiveness and the efficiency of the proposed scheme. The comparative study of AEFA-C is performed with nine state-of-art algorithms, including some IEEE CEC 2017 competitors. The comparative study, statistical analysis, and the findings suggest that the proposed AEFA-C is an efficient constrained optimizer.
55 citations
TL;DR: The experimental results have proven that CSKH algorithm is well capable of solving constrained engineering design problems more efficiently and effectively than the basic CS and KH algorithm.
Abstract: This paper presents a hybrid krill herd (CSKH) approach to solve structural optimization problems CSKH improved the Krill herd algorithm (KH) by combining KU/KA operator originated from cuckoo search algorithm (CS) with KH In CSKH, a greedy selection scheme is used and often overtakes the original KH and CS In addition, in order to further enhance the assessment of CSKH, a fraction of the worst krill is thrown away and substituted with newly randomly generated ones by KA operator at the end of each generation The CSKH is applied to five real engineering problems to verify its performance The experimental results have proven that CSKH algorithm is well capable of solving constrained engineering design problems more efficiently and effectively than the basic CS and KH algorithm
33 citations
Additional excerpts
...The pressure vessel design problem is a widely used benchmark for verifying the performance of the metaheuristic algorithms [17, 44, 56, 73]....
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References
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
01 Dec 2009
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Abstract: In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
5,521 citations
"A Hybrid Flower Pollination Algorit..." refers methods in this paper
...8 Corrugated bulkhead design Corrugated bulkhead design [25,26,23] are often used in chemical tankers and product tankers in order to help facilities cargo tank washing effectively....
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Book•
01 Feb 2008
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
Abstract: Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.
3,626 citations
"A Hybrid Flower Pollination Algorit..." refers background in this paper
...In fact, many nature-inspired algorithms have become very popular, due to their simplicity, flexibility and efficiency [16]....
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TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
3,495 citations
"A Hybrid Flower Pollination Algorit..." refers background or methods in this paper
...1 Handling Constraints The feasible-based mechanism proposed by Deb [17] is used to handle the constraints problem and select the best individuals from one generation according to the following three rules: Rule 1: Between two feasible solutions, the one with the higher fitness value is preferred....
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...The feasible-based mechanism proposed by Deb [17] is used to handle the constraints problem and select the best individuals from one generation according to the following three rules: Rule 1: Between two feasible solutions, the one with the higher fitness value is preferred....
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...For example, in the global pollination step, flower pollen gametes are carried by pollinators such as insects, and pollen can travel over a long distance because insects can often fly and move in a much longer range[17]....
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TL;DR: The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area and the optimal solutions obtained are mostly far better than the best solutions obtained by the existing methods.
Abstract: In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Levy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.
1,701 citations
"A Hybrid Flower Pollination Algorit..." refers methods in this paper
...Four design variables of the problem are width (b), depth (h), length (l), and plate thickness (t) for minimumweight design of the corrugated bulkheads for a tanker, the mathematical formula for the optimization problem as follows [27]:...
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...while using Gandomi et al [27] obtained 5....
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...Results are obtained from FPSA algorithm is better than the results obtained using Gandomi et al [27]....
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