Operations Research

Download Advances in metaheuristics: applications in engineering by Timothy Ganesan, Pandian Vasant, Irraivan Elamvazuthi PDF

By Timothy Ganesan, Pandian Vasant, Irraivan Elamvazuthi

Advances in Metaheuristics: functions in Engineering Systems presents info on present techniques used in engineering optimization. It supplies a accomplished heritage on metaheuristic functions, targeting major engineering sectors equivalent to power, strategy, and fabrics. It discusses themes comparable to algorithmic improvements and function size methods, and gives insights into the implementation of metaheuristic innovations to multi-objective optimization difficulties. With this ebook, readers can learn how to resolve real-world engineering optimization difficulties successfully utilizing the proper ideas from rising fields together with evolutionary and swarm intelligence, mathematical programming, and multi-objective optimization.

The ten chapters of this ebook are divided into 3 elements. the 1st half discusses 3 business purposes within the strength area. the second one focusses on technique optimization and considers 3 engineering purposes: optimization of a three-phase separator, strategy plant, and a pre-treatment strategy. The 3rd and ultimate a part of this ebook covers business purposes in fabric engineering, with a specific concentrate on sand mould-systems. it is usually discussions at the power development of algorithmic features through strategic algorithmic enhancements.

This e-book is helping fill the prevailing hole in literature at the implementation of metaheuristics in engineering purposes and real-world engineering structures. it is going to be a major source for engineers and decision-makers making a choice on and imposing metaheuristics to unravel particular engineering problems.

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2 Parameter Settings of the DE Algorithm Parameters of DE Number of population members Scaling factor Crossover probability constant Number of variables Maximum number of iterations Specified Value P = 30 F = 0�85 CR = 1 D=3 imax = 300 17 Geometric Optimization of Thermoelectric Coolers initial choice (Storn & Price, 1997)� It is said that the value of F smaller than 0�4 and greater than 1 is occasionally effective� To choose the suitable value for crossover probability CR, a bigger CR can increase the convergence speed of the algorithm but a smaller CR could increase the exploitation capability (Guo et al�, 2014)� The value of CR is chosen within the range of [0,1] to help maintain the diversity of the population� However, for most cases, it should be close to 1 (e�g�, CR > 0�9) (Storn & Price, 1997)� When CR is equal to 1, the number of trial solutions will be reduced dramatically� This may lead to search stagnation� Only separable problems do better with CR close to 0 as [0, 0�2] (Price et al�, 2006)� Choosing values for the number of population members, P, is not very critical� An initial guess (10D) is a good choice to obtain global optimum (Guo et al�, 2014), where D stands for a number of variables� Depending on the difficulty of the problem, the number of the population, P, can be lower than (10D) or higher than it to achieve convergence such as 5D to 10D, 3D to 8D, or 2D to 40D (Storn & Price, 1997)� In the stopping condition, the algorithm will stop if number of function evaluations exceeds its maximum value (e�g�, imax = 300).

4 Flowchart of SA algorithm with TEC model� • Step 2: X0 = [A0, L 0, N0] for STEC or [Ih0, Ic0, r0] for TTEC—Initial randomly based point of design parameters within the boundary constraint by computer-generated random numbers method� Then, consider its fitness value as the best fitness so far� • Step 3: Choose a random transition Δx and run = run + 1� • Step 4: Calculate the function value before transition Qc(x) = f (x)� • Step 5: Make the transition as x = x + Δx within the range of boundary constraints� • Step 6: Calculate the function value after transition Qc(x+Δx) = f (x + Δx)� • Step 7: If Δf = f (x + Δx) − f(x) > 0 then accept the state x = x + Δx.

2011), the BFO algorithm was proposed for solving the nonconvex ED� The proposed method was tested on two power systems consisting of 6 and 13 thermal units while considering valve-point effects� The obtained results show that the proposed method had better solution quality, convergence characteristics, computational efficiency, and robustness as compared to other methods� The ABC algorithm proposed by Karaboga in 2005 is a population-based optimization tool (Karaboga, 2005)� The core concept of the ABC algorithm involves the foraging behavior of three types of bees in the honeybee colonies (employed Mean-Variance Mapping Optimization for Economic Dispatch 29 bees, onlooker bees, and scout bees)� Each type of bee has different responsibilities in the colony� The employed bees give information to the onlooker bees about the food sources which they found by swarming� The onlooker bees watch all dances of employed bees and assess the food sources� Then they select one of them for foraging� When a food source is abandoned, some employed bees turn to scout bees� The scout bees search for new food sources in the environment� In the ABC algorithm, the location of a food source indicates a potential solution while the nectar amount in the food source refers to the fitness value (Aydin & Özyön, 2013)� In Hemamalini and Simon (2010), the ABC algorithm was proposed for solving the nonconvex ED problem which considers valve-point effects, MF options, existence of POZs, and ramp-rate limits� The proposed algorithm was tested on the cases consisting of 10, 13, 15, and 40 generating units with nonsmooth cost functions� The comparison of the results with other methods reported in Hemamalini and Simon (2010) proves the superiority of the proposed method� The method is simple, easy to implement, and has a good convergence rate� In Aydin and Özyön (2013), the authors proposed the incremental artificial bee colony approach (IABC) and incremental artificial bee colony with LS technique (IABC-LS)� These approaches were used for solving the ED problem with valve-point effects� The proposed methods were applied to systems with 3, 5, 6, and 40 generators� The results of the algorithms were compared with several other approaches in that work� The obtained results using the proposed methods were seen to be better than the results produced by the other approaches� In the 1990s, the PSO technique was becoming popular in various fields of study (Mahor, Prasad, & Rangnekar, 2009)� PSO is a population-based stochastic search optimization technique motivated by the social behavior of fish schooling and birds flocking� The PSO algorithm searches in parallel using a swarm consisting of a number of particles to explore optimal regions� In PSO, each particle’s position represents an individual potential solution to the optimization problem� Each particle’s position and velocity are randomly initialized in the search space� Each particle then swarms around in a multidimensional search space directed by its own experience and the experience of neighboring particles� PSO can be applied to global optimization problems with nonconvex or nonsmooth objective functions� Recently, PSO is the most post popular method applied for solving ED problems� Several inproved PSO methods and their hybrids have been developed and proposed for solving nonconvex ED problems� In Park et al.

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