Preference modelling by estimating local utility functions for multiobjective optimization

A new neural network‐based multiobjective optimization of water quality management for water pollution control and river basin planning is presented. Past research on water quality management problems has shown that traditional multiobjective decision making does not provide an adequate solution since it depends directly on the decision maker's (DM's) preferences, which may not be clearly defined. In order to overcome the DM's preferences problem this study uses a neural network algorithm to form a model for the solution of the multiobjective problems of water quality management in a river basin. Using the backpropagation algorithm of feedforward neural networks, a multiobjective programming model can simulate the DM's preferences, providing direct help to the analysts involved in real applications. Before describing the details of the multiobjective optimization problem, neural networks are proposed which can be used to predict the DM's preference structures. To demonstrate the procedures and the performance of the neural network‐based approach, the case of the Tou‐Chen River Basin in Taiwan is selected for analysis and discussion.

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

Polynomial goal programming: A procedure for modeling preference trade-offs

ABSTRACTThe Goal Programming (GP) model is one of the first models that have been developed to deal with managerial decision‐making problems where several incommensurable and conflicting objectives are involved. The GP variants integrate the decision maker's preferences differently. This model has also been applied to group decision‐making situations. The aim of this paper is to propose a new typology based on preferences articulation of decision makers through the GP model. This typology is based on the articulation and the elucidation process of the group decision makers' preferences. Copyright © 2012 John Wiley & Sons, Ltd.

The decision maker's preference information on the importance and interaction of decision criteria can be explicitly described by the probabilistic interaction indices in the framework of the capacity based multicriteria decision analysis. In this paper, we first investigate some properties of the probabilistic interaction indices of the empty set, and propose the maximum and minimum empty set interaction principles based capacity identification methods, which can be considered as the comprehensive interaction trend preference information oriented capacity identification methods. Then, by introducing the deviation variables, the goal constraints, as well as the goal objective function, we give a new and more flexible approach to representing the decision maker's explicit preference information on the kind and degree of the interaction of any given combination of decision criteria as well as on the degree of the importance of any decision criterion, and construct the nonempty set interaction indices based capacity identification method, which can be considered as the detailed explicit preference information oriented identification method. Finally, two illustrative examples are respectively given to show the feasibility and applicability of the two kinds of methods. In addition, the comparison analysis between these two kinds of methods and some existing capacity identification methods are also mentioned.

Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

Differential evolutionary algorithm (DE) is a practical and simple intelligent algorithm. Its improvement and application in multi objective problem is the main research of this paper. To improve the convergence speed and stability of DE, and use it to solve multi-objective problems, the mixed mutation strategy, self-adaptive parameters and minimum neighbor distance are used in this paper, aimed for better performance of the algorithm. Combining with these ideals, the multi-objective self-adaptive differential evolution (MOSDE) proposed in this paper is used so solve benchmark test functions and be compared with the SPEA2. The optimization of hybrid electric vehicle (HEV) is a nonlinear and constrained multi-objective optimization problem. For low consumption of fuel and emission load, we use the MOSDE to optimize some components’ parameters and control strategy variables, and provide the best compromise solution from the Pareto solution set.

In this paper, strength Pareto evolutionary algorithm (SPEA) is applied to solve the multiobjective optimal reactive power (VAR) dispatch problem. The optimal VAR dispatch problem is formulated as a non-linear constrained multiobjective optimisation problem where the real power loss and the control variable adjustment costs are to be optimised simultaneously. The proposed approach handles the problem as a true multiobjective optimisation problem. A hierarchical clustering algorithm is imposed to provide the decision-maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise solution over the trade-off curve. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal solutions of the multiobjective VAR dispatch problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multiobjective VAR dispatch problem are confirmed.

To balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a multi-objective White Shark Optimization algorithm (MONSWSO) tailored for multi-objective optimization. MONSWSO integrates non-dominated sorting and crowding distance into the White Shark Optimization framework to select the optimal solution within the population. The uniformity of the initial population is enhanced through a chaotic reverse initialization learning strategy. The adaptive updating of individual positions is facilitated by an elite-guided forgetting mechanism, which incorporates escape energy and eddy aggregation behavior inspired by marine organisms to improve exploration in key areas. To evaluate the effectiveness of MONSWSO, it is benchmarked against five state-of-the-art multi-objective algorithms using four metrics: inverse generation distance, spatial homogeneity, spatial distribution, and hypervolume on 27 typical problems, including 23 multi-objective functions and 4 multi-objective project examples. Furthermore, the practical application of MONSWSO is demonstrated through an example of optimizing the design of subway tunnel foundation pits. The comprehensive results reveal that MONSWSO outperforms the comparison algorithms, achieving impressive and satisfactory outcomes.

Software-defined networking provides a promising solution for traffic engineering (TE) by utilizing a centralized controller to remotely configure programmable network switches. In order to achieve optimal traffic distribution and meet the Quality of Service (QoS) requirements, the SDN controller must frequently solve complex optimization models. Applying an optimization model in ISP (Internet Service Provider) networks implies abundant routing reconfigurations, which adversely impact the network stability and QoS parameters. Namely, since routing updates usually cannot be applied on all switches at the same time, transient congestion and loops often happen as a result. Here, in this paper, we propose a new control framework that strives to maximize the network throughput and provide QoS with minimal reconfiguration costs. In contrast to the conventional TE approaches, which perform the network optimizations periodically and control the side effects of reconfigurations by carefully choosing the period length between the optimization cycles, we propose a multi-objective optimization model which jointly minimizes the routing cost function and the reconfiguration overhead. The Pareto frontier of the optimization model is generated by the augmented ∈-constrained method, whereas a Lyapunov drift-plus-penalty function is used to select the best compromise solution from the Pareto set. Since the reconfiguration overhead is reduced, the network controller could be allowed to optimize resource allocation more frequently, in order to respond quickly and efficiently to the network changes. Considering that the proposed optimization model is computationally intractable in large-scale networks, we also propose a heuristic algorithm to efficiently solve large instances of the problem. In simulations and Mininet experiments, we show that our solution brings performance improvement over the conventional periodic TE techniques. Moreover, the proposed heuristic achieves better QoS request acceptance ratio than the state-of-the-art multi-objective optimization solution, despite the significantly reduced computational complexity.

This paper presents the development of a goal programming (GP) model as an aid to strategic planning and allocation for limited human resources in a health-care organization. The purpose of this study is to assign the personnel to the proper shift hours that enable management to meet the objective of minimizing the total payroll costs while patients are satisfied. A GP model is illustrated using the data provided by a health-care organization in the midwest area. The goals are identified and prioritized. The model result is examined and a sensitivity analysis is performed to improve the model applicability. The GP model application adds insight to the planning functions of resource allocation in the health-care organizations. The proposed model is easily applicable to other human resource planning process.

Simultaneous optimization of two or more objectives is an instance of multi-objective optimization (MOO). However, for most of the Multi-Objective Problems (MOPs), no single solution can optimize all the objective functions simultaneously. Evolutionary algorithms are a type of optimization algorithm used for constructing a well-distributed optimal front more quickly than a much more efficient approach. One of the most commonly used algorithms in this regard is differential evolution (DE). To solve the problem of non-dominated sorting for Multi-Objective DE (MODE), an approach is proposed that reduces the time complexity. DE is commonly recognized as one of the best methods for solving the MOPs. Several versions of DEs have been proposed for MOPs. This article proposes a novel mutation technique, named Environmental Parameter-based Multi-Objective Differential Evolution (EP-MODE) that acquires two additional environmental parameters to preserve diversity and accelerate the convergence. The proposed approach also reduces the time complexity of non-dominated sorting for Multi-Objective DE (MODE). The proposed mutation's performance has been evaluated on DTLZ series and ZDT series benchmark test functions on the Pareto-optimal front and compared with existing multi-objective algorithms (MODE and MODE-RMO). The solution comparatively verifies that the proposed EP-MODE outperforms most of the MODEs for lower dimension functions and higher functions. HIGHLIGHTS New environmental parameters-based mutation has been introduced in a differential evolution algorithm to solve multi-objective optimization problems (EP-mode) The addition of two additional environmental parameters to EP-mode has been found to be appropriate for maintaining diversity while also accelerating the convergence rate EP-MODE evaluated on bi-objective (DTLZ series) and tri-objective (ZDT series) benchmark test functions on the Pareto-optimal front and found EP-MODE's efficiency is higher than that of the MODE algorithm and the MODE algorithm with a ranking-based mutation operator GRAPHICAL ABSTRACT

Multi-objective optimization of a vehicular PEM fuel cell system

Performance –emission optimization of a diesel-hydrogen dual fuel operation: A NSGA II coupled TOPSIS MADM approach

Purpose: To develop an interactive version of the conventional process capability-based approach, called ‘Interactive Process Capability-Based Approach (IPCA)’ in multi-response surface optimization to obtain a satisfactory compromise which incorporates a decision maker(DM)'s preference information precisely. Methods: The proposed IPCA consists of 4 steps. Step 1 is to obtain the estimated process capability indices and initialize the parameters. Step 2 is to maximize the overall process capability index. Step 3 is to evaluate the optimization results. If all the responses are satisfactory, the procedure stops with the most preferred compromise solution. Otherwise, it moves to Step 4. Step 4 is to adjust the preference parameters. The adjustment can be made in two modes: relaxation and tightening. The relaxation is to make the importance of one of the satisfactory responses lower, which is implemented by decreasing its weight. The tightening is to make the importance of one of the unsatisfactory responses higher, which is implemented by increasing its weight. Then, the procedure goes back to Step 2. If there is no response to be adjusted, it stops with the unsatisfactory compromise solution. Results: The proposed IPCA was illustrated through a multi-response surface problem, colloidal gas aphrons problem. The illustration shows that it can generate a satisfactory compromise through an interactive procedure which enables the DM to provide his or her preference information conveniently. Conclusion: The proposed IPCA has two major advantages. One is to obtain a satisfactory compromise which is faithful to the DM preference structure. The other is to make the DM’s participation in the interactive procedure easier by using the process capability index in judging satisfaction/unsatisfaction. The process capability index is very familiar with quality practitioners as well as indicates the process performance levels numerically.