scholarly journals Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-37 ◽  
Author(s):  
Wenping Zou ◽  
Yunlong Zhu ◽  
Hanning Chen ◽  
Beiwei Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Difficult Problem ◽  
Standard Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Flight Direction ◽  
Bee Colony

Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

Interactive Multiobjective Optimization Design Strategy for Decision Based Design

1999 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Procedure ◽  
Design Strategy ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Original Algorithm ◽  
Multi Objective ◽  
Local Preference ◽  
Preference Functions

Abstract This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An Interactive Multi-Objective Optimization Procedure (IMOOP) developed in [12] has been modified in this research to include the DM’s local preference functions in an Iterative Decision Making Strategy (IDMS). This enhanced multiobjective optimization procedure called the interactive MultiObjective Optimization Design Strategy (iMOODS) provides the DM with a formal means for efficient design exploration around a given Pareto point. The use of local preference functions allows the original algorithm [12] to be modified such that the second order Pareto surface approximation is more accurate in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objectives and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

scholarly journals New multiobjective optimization algorithm using NBI-SASP approaches for mechanical structural problems

2022 ◽  
Vol 13 ◽  
pp. 4
Author(s):  
Samira El Moumen ◽  
Siham Ouhimmou
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Structural Problems ◽  
Engineering Design Problems ◽  
Set Of Points

Various engineering design problems are formulated as constrained multi-objective optimization problems. One of the relevant and popular methods that deals with these problems is the weighted method. However, the major inconvenience with its application is that it does not yield a well distributed set. In this study, the use of the Normal Boundary Intersection approach (NBI) is proposed, which is effective in obtaining an evenly distributed set of points in the Pareto set. Given an evenly distributed set of weights, it can be strictly shown that this approach is absolutely independent of the relative scales of the functions. Moreover, in order to ensure the convergence to the Global Pareto frontier, NBI approach has to be aligned with a global optimization method. Thus, the following paper suggests NBI-Simulated Annealing Simultaneous Perturbation method (NBI-SASP) as a new method for multiobjective optimization problems. The study shall test also the applicability of the NBI-SASP approach using different engineering multi-objective optimization problems and the findings shall be compared to a method of reference (NSGA). Results clearly demonstrate that the suggested method is more efficient when it comes to search ability and it provides a well distributed global Pareto Front.

scholarly journals Improving the process performance of magnetic abrasive finishing of SS304 material using multi-objective artificial bee colony algorithm

2020 ◽  
Vol 41 (1) ◽  
pp. 34-49
Author(s):  
Sandip B. Gunjal ◽  
Padmakar J. Pawar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Bee Colony ◽  
Abrasive Finishing

Magnetic abrasive finishing is a super finishing process in which the magnetic field is applied in the finishing area and the material is removed from the workpiece by magnetic abrasive particles in the form of microchips. The performance of this process is decided by its two important quality characteristics, material removal rate and surface roughness. Significant process variables affecting these two characteristics are rotational speed of tool, working gap, weight of abrasive, and feed rate. However, material removal rate and surface roughness being conflicting in nature, a compromise has to be made between these two objective to improve the overall performance of the process. Hence, a multi-objective optimization using an artificial bee colony algorithm coupled with response surface methodology for mathematical modeling is attempted in this work. The set of Pareto-optimal solutions obtained by multi-objective optimization offers a ready reference to process planners to decide appropriate process parameters for a particular scenario.

A Lion’s Pride Inspired Algorithm for VLSI Floorplanning

2019 ◽  
Vol 29 (01) ◽  
pp. 2050003
Author(s):  
Lalin L. Laudis ◽  
N. Ramadass
Keyword(s):  
Design Process ◽  
Integrated Circuit ◽  
Large Scale ◽  
Optimization Problem ◽  
Standard Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Scale Integration ◽  
Vlsi Floorplanning

The complexity of any integrated circuit pushes the researchers to optimize the various parameters in the design process. Usually, the Nondeterministic Polynomial problems in the design process of Very Large Scale Integration (VLSI) are considered as a Single Objective Optimization Problem (SOOP). However, due to the increasing demand for the multi-criterion optimization, researchers delve up on Multi-Objective Optimization methodologies to solve a problem with multiple objectives. Moreover, it is evident from the literature that biologically inspired algorithm works very well in optimizing a Multi-Objective Optimization Problem (MOOP). This paper proposes a new Lion’s pride inspired algorithm to solve any MOOP. The methodologies mimic the traits of a Lion which always strives to become the Pride Lion. The Algorithm was tested with VLSI floorplanning problem wherein the area and dead space are the objectives. The algorithm was also tested with several standard test problems. The tabulated results justify the ruggedness of the proposed algorithm in solving any MOOP.

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