This study introduces a multi-objective version of the recently proposed colonial competitive algorithm (CCA) called multi-objective colonial competitive algorithm. In contrast to original CCA, which used the combinat...
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This study introduces a multi-objective version of the recently proposed colonial competitive algorithm (CCA) called multi-objective colonial competitive algorithm. In contrast to original CCA, which used the combination of the objective functions to solve multi-objective problems, the proposed algorithm incorporates the Pareto concept to store simultaneously optimal solutions of multiple conflicting functions. Another novelty of this paper is the integration of the variable neighborhood search as an assimilation strategy, in order to improve the performance of the obtained solutions. To prove the effectiveness of the proposed algorithm, a set of standard test functions with high dimensions and some multi-objective engineering design problems are investigated. The results obtained amply demonstrate that the proposed approach is efficient and is able to yield a wide spread of solutions with good convergence to true Pareto fronts, compared with other proposed methods in the literature.
Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evol...
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Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive epsilon-dominance (pa epsilon-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constrainthandling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.
This paper proposes an evolutionary alternative to conventional two-phase planning methods for solving the integrated crew scheduling (ICS) problem. The approach models and formulates the ICS problem as a combinationa...
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This paper proposes an evolutionary alternative to conventional two-phase planning methods for solving the integrated crew scheduling (ICS) problem. The approach models and formulates the ICS problem as a combinational optimization problem with multiple constraints and objectives. An integrated evolutionary framework is proposed for simultaneously considering crew pairing and crew rostering subproblems. To improve the efficiency of the Pareto set explorer, the solution methodology applies a novel variant of the nondominated sorting genetic algorithm II (NSGA-II), one of the most popular multiobjective optimization evolutionary algorithms. The proposed variant features problem-dependent constrainthandling and a bounded front policy to reserve diverse individuals. The proposed approach is verified and validated in a case study of a real-world short-haul airline crew scheduling problem. The experimental results obtained by the proposed integrated approach are then compared with a real-world airline plan generated by the conventional sequential method. An aircraft schedule recovery problem is also studied to compare solution performance between the conventional NSGA-II method and the proposed NSGA-II variant. The comparison results confirm that the proposed variant obtains solutions that are superior in two aspects: First, the proposed NSGA-II variant obtains better convergence in the studied problems compared with the original version;second, the results explored by the variant enable decision makers to select from multiple crew schedules, which are superior to the real-world airline crew plan while considering the same objectives and constraints.
The constrained multi-objective optimization problems (CMOPs) is widely used in real-world applications and always hard to handle especially when the objective number becomes more or the constraints are too stringent....
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ISBN:
(纸本)9781665404457
The constrained multi-objective optimization problems (CMOPs) is widely used in real-world applications and always hard to handle especially when the objective number becomes more or the constraints are too stringent. In this manuscript, an improved differential evolution method (IDEM) is proposed based on CMOEA/D as well as newly designed mutation operators. Firstly, one mutation operator is presented to improve infeasible points, in which any infeasible point is taken to divide other points into three groups by using the constraint violation information, and based on the division, a potential better point can be found and utilized to improve other infeasible points by the mutation operation. Then the other mutation operator is provided by designing an objective sorting scheme as well as an individual selection method. These two mutation operators are alternately and self-adaptively adopted in evolution process. Finally, the proposed algorithm is executed on some recent benchmark functions and compared with four state-of-the-art EMO algorithms. The experimental results show that IDEM can efficiently solve the CMOPs.
How to solve constrained optimization problems (COPs) is a significant research issue and we combine the bat-inspired algorithm (BA) with differential evolution (DE) into a new hybrid algorithm called BA-DE for solvin...
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How to solve constrained optimization problems (COPs) is a significant research issue and we combine the bat-inspired algorithm (BA) with differential evolution (DE) into a new hybrid algorithm called BA-DE for solving the COPs. Traditional BAs are prone to sink into stagnation or local optima when no bat individual founds a better location than the past locations for several generations. DE is adopted for updating the past location of bat individuals to force BA to jump out of stagnation or local optima, since it has a great local searching capability. The performance of BA-DE algorithm is improved by the proposed hybrid mechanism. We use 24 well-known benchmark functions to verify the overall performance of our proposed algorithm. Comparisons show that BA-DE outperforms most advanced methods in terms of the final solution's quality.
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