Research of Biogeography-Based Multi-Objective Evolutionary Algorithm

2011 ◽  
Vol 4 (2) ◽  
pp. 70-80 ◽  
Author(s):  
Hongwei Mo ◽  
Zhidan Xu
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Solution Set ◽  
Test Problems ◽  
Benchmark Test ◽  
Multi Objective ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Evolutionary Operators ◽  
External Population

Biogeography-based optimization algorithm (BBO) is an optimization algorithm inspired by the migration of animals in nature. A new multi-objective evolutionary algorithm is proposed, which is called Biogeography-based multi-objective evolutionary algorithm (BBMOEA). The fitness assignment and the external population elitism of SPEA2 are adapted to ensure even distribution of the solution set. The population evolutionary operators of BBO are applied to the evolution of the external population to ensure the convergence of the solution set. Simulation results on benchmark test problems illustrate the effectiveness and efficiency of the proposed algorithm.

Research of Biogeography-Based Multi-Objective Evolutionary Algorithm

2013 ◽  
pp. 125-135 ◽  
Author(s):  
Hongwei Mo ◽  
Zhidan Xu
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Solution Set ◽  
Test Problems ◽  
Benchmark Test ◽  
Multi Objective ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Evolutionary Operators ◽  
External Population

Biogeography-based optimization algorithm (BBO) is an optimization algorithm inspired by the migration of animals in nature. A new multi-objective evolutionary algorithm is proposed, which is called Biogeography-based multi-objective evolutionary algorithm (BBMOEA). The fitness assignment and the external population elitism of SPEA2 are adapted to ensure even distribution of the solution set. The population evolutionary operators of BBO are applied to the evolution of the external population to ensure the convergence of the solution set. Simulation results on benchmark test problems illustrate the effectiveness and efficiency of the proposed algorithm.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

2003 ◽  
Vol 11 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Andrea Toffolo ◽  
Ernesto Benini
Keyword(s):  
Genetic Diversity ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Selection Pressure ◽  
Evaluation Method ◽  
State Of The Art ◽  
Search Space ◽  
Test Problems ◽  
Multi Objective ◽  
Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions

2005 ◽  
Vol 13 (4) ◽  
pp. 501-525 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Manikanth Mohan ◽  
Shikhar Mishra
Keyword(s):  
Steady State ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computational Time ◽  
Test Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Nsga Ii ◽  
Multi Objective

Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the ε-dominance concept introduced earlier (Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the ε-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.

A Preference-Based Multiobjective Evolutioary Algorithm for Finding Knee Solutions

2015 ◽  
Vol 781 ◽  
pp. 559-563 ◽  
Author(s):  
Sufian Sudeng ◽  
Naruemon Wattanapongsakorn
Keyword(s):  
Evolutionary Algorithm ◽  
Decision Maker ◽  
Test Problems ◽  
Optimal Solutions ◽  
View Point ◽  
Controller Parameter ◽  
Multi Objective

The aim of this paper is to develop a knee-based Multi-Objective Evolutionary Algorithm (MOEA) which is a method to find optimal solutions focusing on knee regions. The knee solutions are very interesting to the decision maker (DM) when he/she does not have an explicit preference. The proposed approach uses the extended angle-based dominance concept to guide the search towards knee regions. The extent of the obtained solutions can be controlled by the means of user-supplied density controller parameter. The approach is demonstrated with two and three-objective knee-based test problems. The results have shown that our approach is competitive to well-known knee-based MOEAs in convergence view point.

MOEA/D for Multi-Objective Hybrid Flowshop Rescheduling Problem

2018 ◽  
Author(s):  
Biao Zhang ◽  
Quan-ke Pan ◽  
Liang Gao ◽  
Yao-bang Zhao
Keyword(s):  
Evolutionary Algorithm ◽  
Completion Time ◽  
Production Efficiency ◽  
State Of The Art ◽  
Weighted Sum ◽  
Computational Results ◽  
Hybrid Flowshop ◽  
Multi Objective ◽  
Effectiveness And Efficiency ◽  
Maximum Completion Time

In this paper, a multi-objective hybrid flowshop rescheduling problem (HFRP) is addressed in a dynamic shop environment where two types of real-time events, namely machine breakdown and job cancellation, simultaneously happen. For the addressed problem, two objectives are considered. One objective concerning the production efficiency is minimizing the maximum completion time or makespan, while regarding with the instability, the total number of the jobs assigned to different machines between the revised and the origin schedule is considered. A multi-objective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve this problem. In the algorithm, the weighted sum approach is used as the decomposition strategy. The algorithm is, then, rigorously compared with three state-of-the-art evolutionary multi-objective optimizers, and the computational results demonstrate the effectiveness and efficiency of the algorithm.

scholarly journals Multi-objective gate assignment based on robustness in hub airports

2017 ◽  
Vol 9 (2) ◽  
pp. 168781401668858 ◽  
Author(s):  
Hong-Hai Zhang ◽  
Qing-Wen Xue ◽  
Yu Jiang
Keyword(s):  
Optimization Algorithm ◽  
New Method ◽  
Optimal Solutions ◽  
Interval Time ◽  
Multi Objective ◽  
Assignment Model ◽  
Proposed Model ◽  
Simulation Results ◽  
Gate Assignment

To enhance the robustness of the gate assignment, reduce the possibility of flight conflict, and improve the quality of passenger services, a multi-objective gate assignment model is proposed by minimizing flight conflict probability and number of flights assigned to aprons. The biogeography-based optimization algorithm is used to solve the proposed model with a new method for estimating the conflict probability. The simulation results show that the ratio of interval time of two flights assigned to the same gate between 60 and 120 min is as high as 82% when the rate of the flights assigned to aprons is controlled below 0.4. This means that the robustness increases greatly, and the probability of flight conflicts decreases, which is beneficial to the implement of flight assignment plan. In addition, the biogeography-based optimization algorithm is more effective to solve the proposed model and very easy to find out the optimal solutions.

scholarly journals Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm

2013 ◽  
Vol 18 (3) ◽  
pp. 293-313 ◽  
Author(s):  
Algirdas Lančinskas ◽  
Pilar Martinez Ortigosa ◽  
Julius Žilinskas
Keyword(s):  
Genetic Algorithm ◽  
Experimental Investigation ◽  
Optimization Algorithm ◽  
Single Agent ◽  
Stochastic Search ◽  
Local Optimization ◽  
Test Problems ◽  
Stochastic Local Search ◽  
Multi Objective Optimization ◽  
Multi Objective

A hybrid multi-objective optimization algorithm based on genetic algorithm and stochastic local search is developed and evaluated. The single agent stochastic search local optimization algorithm has been modified in order to be suitable for multi-objective optimization where the local optimization is performed towards non-dominated points. The presented algorithm has been experimentally investigated by solving a set of well known test problems, and evaluated according to several metrics for measuring the performance of algorithms for multi-objective optimization. Results of the experimental investigation are presented and discussed.

scholarly journals A Novel Multi-Objective Artificial Bee Colony Algorithm for the QoS Based Wireless Route Optimization Problem

2012 ◽  
Vol 6-7 ◽  
pp. 445-451
Author(s):  
Chang Sheng Zhang ◽  
Ming Kang Ren ◽  
Bin Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Route Optimization ◽  
Test Problems ◽  
Nsga Ii ◽  
Solution Quality ◽  
Artificial Bee Colony Optimization ◽  
Multi Objective ◽  
Bee Colony

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distributed solutions. The proposed algorithm was evaluated on a set of different scale test problems and compared with the recently proposed popular NSGA-II based multi-objective optimization algorithm. The experimental results reveal very encouraging results in terms of the solution quality and the processing time required.

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