Two hybrid differential evolution algorithms for engineering design optimization

Costa

Computational Science and Its Applications – ICCSA 2014

2014

Abstract. A mixed-integer nonlinear programming problem (MINLP) is a problem with continuous and integer variables and at least, one nonlinear function. This kind of problem appears in a wide range of real applications and is very difficult to solve. The difficulties are due to the nonlinearities of the functions in the problem and the integrality restrictions on some variables. When they are nonconvex then they are the most difficult to solve above all. We present a methodology to solve nonsmooth nonconvex MINLP problems based on a branch and bound paradigm and a stochastic strategy. To solve the relaxed subproblems at each node of the branch and bound tree search, an algorithm based on a multistart strategy with a coordinate search filter methodology is implemented. The produced numerical results show the robustness of the proposed methodology.

“…Finally, we compare our results with those reported in [19]. This paper presents two hybrid differential evolution (DE) algorithms.…”

Section: Numerical Resultsmentioning

confidence: 54%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Branch and Bound Based Coordinate Search Filter Algorithm for Nonsmooth Nonconvex Mixed-Integer Nonlinear Programming Problems

Costa

Computational Science and Its Applications – ICCSA 2014

2014

“…Liao [11] proposed two versions of ant colony optimization (ACOr and ACO-S) based algorithms for feature selection and applied them to computeraided weld inspections. Liao [12] also investigated feature extraction and feature selection in sensor-based condition monitoring during grinding operations of ceramic materials.…”

Section: Many Applications Of Metaheuristics In Electricalmentioning

confidence: 99%

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Hossain¹

2017

IJARA

In die manufacturing industries surface roughness is considered as a vital quality characteristic in order to retain the consumers' satisfaction. On the other hand, manufacturers want to minimize the machining time which eventually reduces their cost. This research deals with an optimization problem to minimize the machining time (T) for end milling operation on hot die steel (H13), subject to specified surface roughness (R a ) limits. Six machining parameters and corresponding T and R a were recorded from 74 independent experiments. After exhaustive search, three machining parameters (tool inclination angle, tool diameter and radial depth of cut) for R a and two machining parameters (feed rate and radial depth of cut) for T are found to be highly influential. In terms of these corresponding parameters, two ANFIS models are developed for the prediction of R a and T, respectively. These models are utilized to find the optimum values of machining parameters. Five advanced metaheuristic algorithms, Artificial Bee Colony (ABC), quick artificial bee colony, modified differential evolution, ant colony optimization for real numbers and simulated annealing, with or without local search, are applied for solving this optimization problem. Each of the algorithms is run for 30 times, allowing 100,000 number of function evaluations in each run. Statistical analysis (F-test, t-test) are done to evaluate the performance of the algorithms. Hybrid ABC with local search is proposed as the best algorithm for solving this problem based on average of minimum machining time obtained. The proposed optimization approach can be used for parameter selection in real time machining with artificially intelligent Computer Numerical Control (CNC) machine tools.

“…In evolutionary calculations, combination is essential in order to improve an optimization algorithm performance. Generally, an algorithm performance is lower than combinational algorithm [4,5]. In this paper, a combinational strategy of combining two PSO and ACOR algorithms is proposed that solves greatly problems of ACOR and PSO algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Of Particle Swarm Optimization And Continuous Ant Colony Optimization For Multimodal Functions Optimization

Fazeli¹,

Abadi²,

Rezaei³

2015

J. Math. Computer Sci.

In many real-world optimization problems, multimodal function optimization is considered, in fact, we are faced with a multimodal optimization problem. Particle swarm optimization (PSO) and continuous ant colony optimization (ACOR) are two population-based optimization techniques that work based on probability laws. The main problem of PSO and ACOR algorithms is premature convergence and falling into local optima. One way to solve the problems is to use combinational methods. This paper presents a combinational method including PSO and ACOR in order to improve the search process. The proposed algorithm tries to solve the problem. Standard benchmark functions are used in order to evaluate the proposed algorithm, proposed method was compared with ACOR, PSO and sequential approach with the enlarged pheromone-particle table of the composition of PSO and ACOR Introduced in [8]. Results show that the proposed method is superior.