Sufian Sudeng

Post Pareto-optimal pruning algorithm for multiple objective optimization using specific extended angle dominance

For the last two decades, significant effort has been devoted to exploring Multi-Objective Evolutionary Algorithms (MOEAs) for solving complex practical optimization problems. MOEAs approximate a representative set of Pareto-optimal solutions and present them to the decision maker (DM). Recently, studies in this area have focused on decision-making techniques in order to help the DM to arrive at a single preferred solution. This paper presents a pruning algorithm which can be applied in the post Pareto-optimal phase to select a subset of robust Pareto-optimal solutions before presenting them to the DM. Our algorithm is called Angle based with Specific bias parameter pruning Algorithm (ASA). Our pruning method begins by calculating the angle between each pair of solutions using an arctangent function. We introduce a bias intensity parameter to calculate a threshold angle in order to identify areas with desirable solutions based on the DM׳s preference. The bias parameter can be tuned specifically for each objective. We also propose a technique to determine a region of interest using reference point to MOEA/D algorithm which leads to a modified version of MOEA/D (PR-MOEA/D). The experimental results show that our pruning algorithm provides a robust subset of Pareto-optimal solutions for our benchmark problems when evaluating solutions in terms of convergence to optimality.

Finding Robust Pareto-optimal Solutions Using Geometric Angle-Based Pruning Algorithm

Evolutionary multi-objective optimization algorithms have been developed to find a representative set of Pareto-optimal solutions in the past decades. However, researchers have pointed out that finding a representative set of Pareto-optimal solutions is not sufficient; the task of choosing a single preferred Pareto-optimal solution is also another important task which has received a widespread attention so far. In this paper, we propose an algorithm to help the decision maker (DM) choose the final preferred solution based on his/her preferred objectives. Our algorithm is called an adaptive angle based pruning algorithm with independent bias intensity tuning parameter (ADA-τ). The method begins by calculating the angle between a pair of solutions by using a simple arctangent function. The bias intensity parameter of each objective is introduced independently in order to approximate the portions of desirable solutions based on the DM’s preferred objectives. We consider several benchmark problems including two and three-objective problems. The experimental results have shown that our pruning algorithm provides a robust sub-set of Pareto-optimal solutions for the benchmark problems.

A knee-based multi-objective evolutionary algorithm: an extension to network system optimization design problem

High performance computing (HPC) research is confronted with multiple competing goals such as reducing makespan and reducing cost in clouds. These competing goals must be optimized simultaneously. Multi-objective optimization techniques to tackle such HPC problems have received significant research attention. Most multi-objective optimization approaches provide a large number of potential solutions. Choosing the best or most preferred solution becomes a problem. In some practical contexts, even if the decision maker does not have an explicit preference, there exist the regions of the solution space that can be viewed as implicitly preferred because of the way the problem has been formulated. Solutions located in these regions are called “knee solutions”. Evolutionary approaches have become popular and effective in solving complex and large problems that require HPC. The aim of this paper is to develop a knee-based multi-objective evolutionary algorithm (MOEA) which can prune the set of optimal solutions with a controllable parameter to focus on knee regions. The proposed approach uses a concept called extended dominance to guide the solution process towards knee regions. A user-supplied density controller parameter determines the number of preferred solutions retained. We verify our approach using two and three-objective knee-based test problems. The results show that our approach is competitive with other well-known knee-based MOEAs when evaluated by a convergence metric. We then apply the approach to a network optimization design problem in order to demonstrate how it can be useful in a practical context related to HPC.

A Preference-Based Multi-Objective Evolutionary Algorithm for Redundancy Allocation Problem

Allowing the decision maker (DM) incorporates his/her preferences in Multiobjective Evolutionary Algorithm (MOEA) is likely yield better approximation of optimal trade-off solutions for multi-objective optimization problem (MOP). In this paper, we propose a preference-based MOEA to help the DM choosing the final best solution(s) based on his/her preferred objective(s). Our algorithm is called ASA-NSGA-II. The approach is accomplished by replacing the crowding estimator technique in NSGA-II algorithm by applying an extended angle-based dominance technique. We consider Redundancy Allocation Problem (RAP) to observe the usefulness of our algorithm in practical context. The system composes of multiple subsystems connected in series. The designed objective is to select multiple components for a subsystem. The objective functions are to maximize system reliability (R), minimize system cost (C), and minimize system weight (W), simultaneously. We demonstrate seven cases of preferences with interesting results.

Multi-objective optimization and decision making for greenhouse climate control system considering user preference and data clustering

Optimization of the systems can increase their efficiency with appropriate system performance indices. Nowadays, climate control of industrial greenhouses consists of many control parameters such as light, temperature, CO

A decomposition-based approach for knee solution approximation in multi-objective optimization

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Pruning algorithm for Multi-objective optimization

2 concentration, humidity, and etc. Most of these systems use the PID control structure due to its simplicity, flexibility, and good performance. The electrical lamps and heaters can be used to provide appropriate light and temperature inside the greenhouse. Optimal tuning of electrical lamps and heaters control system has a significant influence on efficiency and performance improvement of greenhouse cultivation system. For this aim, NSGA-II, a well-known evolutionary algorithm, is applied for the system optimization verified with an exhaustive search approach. Making a final decision to choose the best solution among the optimal solutions is a challenging decision making issue. In this regard, post Pareto-optimal pruning algorithms are employed considering various user preferences and clustering approaches. The final results show and verify the substantial improvement of greenhouse climate control system efficiency and performance.

Finding Knee Solutions in Multi-Objective Optimization Using Extended Angle Dominance Approach

Optimization of the real-world problems has been always considered by researchers due to its substantial improvement effect on processes. Modeling, simulation and decision making are three major parts of all optimization approaches. In this paper, Tri-objective optimization of an industrial greenhouse climate control system is investigated. In this regard, the greenhouse is modeled by the real measured data. Optimization and decision making are obtained by MATLAB simulator environment and using evolutionary and pruning algorithms. The simulation results verify the significant improvement of greenhouse climate control system efficiency and performance.