Constrained Multi-objective Optimization: Test Problem Construction and Performance Evaluations

2020 ◽  
pp. 1-1
Author(s):  
Yuren Zhou ◽  
Yi Xiang ◽  
Xiaoyu He
Keyword(s):  
Test Problem ◽  
Performance Evaluations ◽  
Multi Objective Optimization ◽  
Problem Construction ◽  
Multi Objective ◽  
And Performance

scholarly journals The dilemma between eliminating dominance-resistant solutions and preserving boundary solutions of extremely convex Pareto fronts

2021 ◽  
Author(s):  
Zhenkun Wang ◽  
Qingyan Li ◽  
Qite Yang ◽  
Hisao Ishibuchi
Keyword(s):  
Evolutionary Algorithms ◽  
Coping Strategies ◽  
Test Problem ◽  
Pareto Front ◽  
Optimization Problems ◽  
Feasible Region ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Fronts ◽  
Boundary Solutions

AbstractIt has been acknowledged that dominance-resistant solutions (DRSs) extensively exist in the feasible region of multi-objective optimization problems. Recent studies show that DRSs can cause serious performance degradation of many multi-objective evolutionary algorithms (MOEAs). Thereafter, various strategies (e.g., the $$\epsilon $$ ϵ -dominance and the modified objective calculation) to eliminate DRSs have been proposed. However, these strategies may in turn cause algorithm inefficiency in other aspects. We argue that these coping strategies prevent the algorithm from obtaining some boundary solutions of an extremely convex Pareto front (ECPF). That is, there is a dilemma between eliminating DRSs and preserving boundary solutions of the ECPF. To illustrate such a dilemma, we propose a new multi-objective optimization test problem with the ECPF as well as DRSs. Using this test problem, we investigate the performance of six representative MOEAs in terms of boundary solutions preservation and DRS elimination. The results reveal that it is quite challenging to distinguish between DRSs and boundary solutions of the ECPF.

scholarly journals A rapid design space exploration approach for multi-objective optimization of DSP filter designs

2021 ◽  
Author(s):  
Aakriti Tarun Sharma
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Systems Design ◽  
Design Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Time Period ◽  
And Performance ◽  
High Level

The process of converting a behavioral specification of an application to its equivalent system architecture is referred to as High Level-Synthesis (HLS). A crucial stage in embedded systems design involves finding the trade off between resource utilization and performance. An exhaustive search would yield the required results, but would take a huge amount of time to arrive at the solution even for smaller designs. This would result in a high time complexity. We employ the use of Design Space Exploration (DSE) in order to reduce the complexity of the design space and to reach the desired results in less time. In reality, there are multiple constraints defined by the user that need to be satisfied simultaneously. Thus, the nature of the task at hand is referred to as Multi-Objective Optimization. In this thesis, the design process of DSP benchmarks was analyzed based on user defined constraints such as power and execution time. The analyzed outcome was compared with the existing approaches in DSE and an optimal design solution was derived in a shorter time period.

scholarly journals A rapid design space exploration approach for multi-objective optimization of DSP filter designs

2021 ◽  
Author(s):  
Aakriti Tarun Sharma
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Systems Design ◽  
Design Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Time Period ◽  
And Performance ◽  
High Level

The process of converting a behavioral specification of an application to its equivalent system architecture is referred to as High Level-Synthesis (HLS). A crucial stage in embedded systems design involves finding the trade off between resource utilization and performance. An exhaustive search would yield the required results, but would take a huge amount of time to arrive at the solution even for smaller designs. This would result in a high time complexity. We employ the use of Design Space Exploration (DSE) in order to reduce the complexity of the design space and to reach the desired results in less time. In reality, there are multiple constraints defined by the user that need to be satisfied simultaneously. Thus, the nature of the task at hand is referred to as Multi-Objective Optimization. In this thesis, the design process of DSP benchmarks was analyzed based on user defined constraints such as power and execution time. The analyzed outcome was compared with the existing approaches in DSE and an optimal design solution was derived in a shorter time period.

Patch size setup and performance/cost trade-offs in multi-objective EM-driven antenna optimization using sequential domain patching

2017 ◽  
Vol 34 (4) ◽  
pp. 1070-1081
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Control Parameter ◽  
Computational Cost ◽  
Design Procedure ◽  
Simulation Models ◽  
Multi Objective Optimization ◽  
Computationally Efficient ◽  
Content Type ◽  
Multi Objective ◽  
Trade Offs ◽  
And Performance

Purpose This paper aims to assess control parameter setup and its effect on computational cost and performance of deterministic procedures for multi-objective design optimization of expensive simulation models of antenna structures. Design/methodology/approach A deterministic algorithm for cost-efficient multi-objective optimization of antenna structures has been assessed. The algorithm constructs a patch connecting extreme Pareto-optimal designs (obtained by means of separate single-objective optimization runs). Its performance (both cost- and quality-wise) depends on the dimensions of the so-called patch, an elementary region being relocated in the course of the optimization process. The cost/performance trade-offs are studied using two examples of ultra-wideband antenna structures and the optimization results are compared to draw conclusions concerning the algorithm robustness and determine the most advantageous control parameter setups. Findings The obtained results indicate that the investigated algorithm is very robust, i.e. its performance is weakly dependent on the control parameters setup. At the same time, it is found that the most suitable setups are those that ensure low computational cost, specifically non-uniform ones generated on the basis of sensitivity analysis. Research limitations/implications The study provides recommendations for control parameter setup of deterministic multi-objective optimization procedure for computationally efficient design of antenna structures. This is the first study of this kind for this particular design procedure, which confirms its robustness and determines the most suitable arrangement of the control parameters. Consequently, the presented results permit full automation of the surrogate-assisted multi-objective antenna optimization process while ensuring its lowest possible computational cost. Originality/value The work is the first comprehensive validation of the sequential domain patching algorithm under various scenarios of its control parameter setup. The considered design procedure along with the recommended parameter arrangement is a robust and computationally efficient tool for fully automated multi-objective optimization of expensive simulation models of contemporary antenna structures.

Sistem Pendukung Keputusan Penentuan Ranking Kenaikan Gaji Staff Dan Karyawan

2019 ◽  
Vol 3 (2) ◽  
pp. 167
Author(s):  
Poningsih Poningsih
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Employee Performance ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Work Period ◽  
Multi Objective ◽  
And Performance ◽  
Performance Ranking

Currently AMIK and STIKOM Tunas Bangsa have approximately 100 employees (staff and employees). Each employee has a different and varied salary. Every year, AMIK and STIKOM Tunas Bangsa management provide salary increases to their employees. But the number of increases is very diverse. This decision support system will later provide recommendations to management in the form of employee performance ranking. There are several factors used in this decision support system, including work period, education and performance. The method used is Multi-objective Optimization on The Base of Ratio Analysis (MOORA). Where the advantages of MOORA are having a good level of selectivity because it can determine the objectives of the conflicting criteria.

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