Physics-Inspired Optimization Algorithms: A Survey

Biswas, Anupam; Mishra, K. K.; Tiwari, Shailesh; Misra, A. K.

doi:10.1155/2013/438152

Cited by 109 publications

(58 citation statements)

References 141 publications

(143 reference statements)

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“…The velocity and coordinates of an individual i at time t þ 1 are calculated via Eq. 24.3 Biswas et al 2013): where the kth components of individual i's velocity and coordinates at t iteration is denoted by v i;k t ð Þ and x i;k t ð Þ, respectively.…”

Section: Fundamentals Of Artificial Physics Optimization Algorithmmentioning

confidence: 99%

“…Several APO applications and variants can also be found in the literature Popov 2012, 2013; Zeng 2009b, 2011;Mo and Zeng 2009; Wang and Zeng 2010a, b;Yang et al 2010;Yin et al 2010; Xie et al 2011c, d;Wang et al 2011). To implement the APO algorithm, the following steps need to be performed Biswas et al 2013):• Initialization step: At this step, a swarm of individuals is randomly generated in the n-dimensional decision space. 376 24 Emerging Physics-based CI Algorithms…”

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confidence: 99%

“…In APO, the mass is defined via Eq. 24.1 Biswas et al 2013):The force is then calculated through Eq. 24.2 Biswas et al 2013):…”

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confidence: 99%

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Emerging Physics-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, a set of (more specifically 22 in total) emerging physicsbased computational intelligence (CI) algorithms are introduced. We first, in Sect. 24.1, describe the organizational structure of this chapter. Then, from Sects. 24.2 to 24.23, each section is dedicated to a specific algorithm which falls within this category. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 24.24 closes this chapter. IntroductionSeveral novel physics-based algorithms were detailed in previous chapters. In particular, Chap. 18 detailed the big bang-big crunch algorithm, Chap. 19 was dedicated to central force optimization algorithm, Chap. 20 discussed the charged system search algorithm, Chap. 21 introduced the electromagnetism-like mechanism algorithm, Chap. 22 was devoted to the gravitational search algorithm, and Chap. 23 described the intelligent water drops algorithm. Apart from this quasimature physics principles inspired CI methods, there are some emerging algorithms also fall within this category. This chapter collects 22 of them that are currently scattered in the literature and organizes them as follows: The effectiveness of these newly developed algorithms are validated through the testing on a wide range of benchmark functions and engineering design problems, and also a detailed comparison with various traditional performance leading CI algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), evolutionary algorithm (EA), fuzzy system (FS), ant colony optimization (ACO), and simulated annealing (SA). Artificial Physics Optimization AlgorithmIn this section, we will introduce an emerging CI algorithm that is based on artificial physics or physicomimetics, a concept which was introduced in Spears et al. (2004a, b); Spears and Gordon (1999); Spears and Spears (2012). Fundamentals of Artificial Physics Optimization AlgorithmArtificial physics optimization (APO) algorithm was recently proposed in Xie et al. ( , b, 2010aXie et al. ( , b, 2011a, . Several APO applications and variants can also be found in the literature Popov 2012, 2013; Zeng 2009b, 2011;Mo and Zeng 2009; Wang and Zeng 2010a, b;Yang et al. 2010;Yin et al. 2010; Xie et al. 2011c, d;Wang et al. 2011). To implement the APO algorithm, the following steps need to be performed Biswas et al. 2013):• Initialization step: At this step, a swarm of individuals is randomly generated in the n-dimensional decision space. 376 24 Emerging Physics-based CI Algorithms• Force calculation step: At this step, according to the masses and distances between individual particles, the total force exerted on each particle is computed. In APO, the mass is defined via Eq. 24.1 Biswas et al. 2013):The force is then calculated through Eq. 24.2 Biswas et al. 2013):ð24:2ÞThe kth component of the total force F i;k exerted on individual i by all other individuals is acquired via Eq. 24.3...

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Section: Fundamentals Of Artificial Physics Optimization Algorithmmentioning

confidence: 99%

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confidence: 99%

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Emerging Physics-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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“…In the year 1995 Kennedy and Eberhart anticipated PSO. The concept of particle best and global best introduce the memory concept in PSO and make this algorithm more fast and better as compared to GA [15], [20]. Owing to its unfussiness, greater convergence characteristics as well as high precision, PSO proves its effectiveness for complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Nature inspired algorithm using particle swarm approach with variations in inertia weights for automatic test data generation based on dominance concepts

Singla¹,

Kumar²

2017

IJET

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In software testing, testing of all program statements is a very crucial issue as it consumes a lot of time, effort and cost. The time, effort and cost can be reduced by using an efficient technique to reduce the test case and a good optimization algorithm to generate efficient, reliable and unique test cases. In this paper, the concept of dominance tree is used which covers all edges/statement by using minimum test case. Nature inspired algorithm -PSO (Particle Swarm Optimization) by applying different inertia weights is used to generate unique, reliable and efficient test cases to cover the leaf nodes of dominance tree. Inertia weights like fixed inertia weight (FIW), globallocal best (GLbestIW), Time-Dependent weight (TDW), and proposed GLbestRandIW weights are used with PSO to investigate the effect of inertia weights on the execution of PSO with respect to number of generation required, percentage coverage , total test cases generated to test the software under consideration.

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“…Simulated Annealing (SA) [10], Artificial Physics Optimization (APO) [11,12], Central Force Optimization (CFO) [13], Harmony Search Algorithm (HS) [14], Space Gravitation optimization [15] etc. are physics inspired heuristic search algorithms [16]. These algorithms mimic physical behaviour and physical principals.…”

Section: Introductionmentioning

confidence: 99%

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

Singh¹,

Deep²

2015

IJISA

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Abstract-The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

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Physics-Inspired Optimization Algorithms: A Survey

Cited by 109 publications

References 141 publications

Emerging Physics-based CI Algorithms

Emerging Physics-based CI Algorithms

Nature inspired algorithm using particle swarm approach with variations in inertia weights for automatic test data generation based on dominance concepts

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

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