An Improved Population Migration Algorithm for Solving Multi-Objective Optimization Problems
نویسندگان
چکیده
The population migration algorithm is a very effective evolutionary algorithm for solving single-objective optimization problems, but very few applications are available for solving multi-objective optimization problems (MOPs). The current study proposes an improved population migration algorithm for solving MOPs based on the vector evaluated method and the dynamic weighted aggregation. The local search ability of the improved algorithm is greatly increased by using the population flow mode. The convergence of the improved algorithm is also proven. Performance metrics and experimental test results show that the improved algorithm is very feasible and effective for solving MOPs.
منابع مشابه
Optimization of Thermal Instability Resistance of FG Flat Structures using an Improved Multi-objective Harmony Search Algorithm
This paper presents a clear monograph on the optimization of thermal instability resistance of the FG (functionally graded) flat structures. For this aim, two FG flat structures, namely an FG beam and an FG circular plate, are considered. These structures are assumed to obey the first-order shear deformation theory, three-parameters power-law distribution of the constituents, and clamped bounda...
متن کاملA Hybrid MOEA/D-TS for Solving Multi-Objective Problems
In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving multi-objective optimization problems (MOPs) with two or three conflicting objectives. This i...
متن کاملَA Multi-objective simulated annealing algorithm to solving flexible no-wait flowshop scheduling problems with transportation times
This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presen...
متن کاملAn Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems
Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and ...
متن کاملSolving Multi-objective Optimal Control Problems of chemical processes using Hybrid Evolutionary Algorithm
Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. This paper applies an evolutionary optimization scheme, inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Int. J. Computational Intelligence Systems
دوره 5 شماره
صفحات -
تاریخ انتشار 2012