By Timothy Ganesan, Pandian Vasant, Irraivan Elamvazuthi
Advances in Metaheuristics: purposes in Engineering Systems presents information on present methods used in engineering optimization. It offers a finished heritage on metaheuristic purposes, concentrating on major engineering sectors similar to strength, strategy, and fabrics. It discusses issues corresponding to algorithmic improvements and function size methods, and offers insights into the implementation of metaheuristic concepts to multi-objective optimization difficulties. With this booklet, readers can learn how to clear up real-world engineering optimization difficulties successfully utilizing the suitable options from rising fields together with evolutionary and swarm intelligence, mathematical programming, and multi-objective optimization.
The ten chapters of this booklet are divided into 3 elements. the 1st half discusses 3 commercial functions within the power zone. the second one focusses on method optimization and considers 3 engineering purposes: optimization of a three-phase separator, procedure plant, and a pre-treatment method. The 3rd and ultimate a part of this e-book covers commercial purposes in fabric engineering, with a selected specialise in sand mould-systems. it is also discussions at the power development of algorithmic features through strategic algorithmic enhancements.
This ebook is helping fill the present hole in literature at the implementation of metaheuristics in engineering functions and real-world engineering platforms. will probably be a massive source for engineers and decision-makers picking and imposing metaheuristics to unravel particular engineering problems.
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Extra info for Advances in metaheuristics: applications in engineering systems
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.
3 Parameter Settings of SA Algorithm No. 1 2 3 4 5 6 7 Parameter Settings Specific Values Initial temperature Maximum number of runs Maximum number of acceptance Maximum number of rejections Temperature reduction value Boltzmann annealing Stopping criteria T0 = 100 runmax = 250 accmax = 125 rejmax = 125 α = 0�95 kB = 1 Tfinal = 10−10 local minima and is thus able to explore globally for more possible solutions� An annealing schedule is selected to systematically decrease the temperature as the algorithm proceeds� As the temperature decreases, the algorithm reduces the extent of its search to converge to a minimum� A programmed SA code was used and its parameters were adjusted so that it could be utilized for finding the optimal TEC design� Choosing good algorithm parameters is very important because it greatly affects the whole optimization process� Parameter settings of SA are listed in Table 1�3� The initial temperature, T0 = 100, should be high enough such that in the first iteration of the algorithm, the probability of accepting a worse solution, is at least 80%� The temperature is the controlled parameter in SA and it is decreased gradually as the algorithm proceeds (Vasant & Barsoum, 2009)� Temperature reduction value α = 0�95 and temperature decrease function is: Tn = αTn−1 (1�39) The numerical experimentation was done with different α values: 0�70, 0�75, 0�85, 0�90, and 0�95 (Abbasi, Niaki, Khalife, & Faize, 2011)� Boltzmann annealing factor, k B, is used in the Metropolis algorithm to calculate the acceptance probability of the points� Maximum number of runs, run max = 250, determines the length of each temperature level T · accmax = 125 determines the maximum number of acceptance of a new solution point and rejmax = 125 determines the maximum number of rejection of a new solution point (run max = accmax + rejmax) (Abbasi et al�, 2011)� The stopping criteria determine when the algorithm reaches the desired energy level� The desired or final stopping temperature is set as Tfinal = 10−10� The SA algorithm is described in the following section and the flowchart of SA algorithm is shown in Figure 1�4� • Step 1: Set the initial parameters and create initial point of the design variables� For SA algorithm, determine required parameters for the algorithm as in Table 1�3� For TEC device, set required parameters such as fixed parameters and boundary constraints of the design variables, and set all the constraints and apply them into penalty function� 20 Advances in Metaheuristics: Applications in Engineering Systems Start Determine required parameters for STEC device and SA algorithm Initialize a random base point of design variable X0 Update T with function Tn = α .
Tn–1 Choose a random transition ∆x run = run + 1; Calculate Qc(x) = f (x) x = x + ∆x Qc(x+∆x) = f (x + Δ x) No No ∆f = f (x+∆x) – f (x) >0 Yes No e[ f (x+∆ x)–f (x)]/(kBT ) > rand(0,1) No Yes Accept x = x + ∆ x acc = acc + 1; acc ≥ accmax or run ≥ runmax ? Yes Stopping conditions meet? 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.
Advances in metaheuristics: applications in engineering systems by Timothy Ganesan, Pandian Vasant, Irraivan Elamvazuthi