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For one thing, despite the fact that it is popular to research the minimization of the power losses in power systems, the optimization of single objective seems insufficient to fully improve the performance of power systems. Multi-objective VAR Dispatch (MVARD) generally minimizes two objectives simultaneously: power losses and voltage deviation. The purpose of this paper is to propose Multi-Objective Enhanced PSO (MOEPSO) algorithm that achieves a good performance when applied to solve MVARD problem. Thus, the new algorithm is worthwhile to be known by the public.

Motivated by differential evolution algorithm, cross-over operator is introduced to increase particle diversity and reinforce global searching capacity in conventional PSO. In addition to that, a constraint-handling approach considering Constrain-prior Pareto-Dominance (CPD) is presented to handle the inequality constraints on dependent variables. Constrain-prior Nondominated Sorting (CNS) and crowding distance methods are considered to maintain well-distributed Pareto optimal solutions. The method combining CPD approach, CNS technique, and cross-over operator is called the MOEPSO method.

The IEEE 30 node and IEEE 57 node on power systems have been used to examine and test the presented method. The simulation results show the MOEPSO method can achieve lower power losses, smaller voltage deviation, and better-distributed Pareto optimal solutions comparing with the Multi-Objective PSO approach.

The most original parts include: the presented MOEPSO algorithm, the CPD approach that is used to handle constraints on dependent variables, and the CNS method which is considered to maintain a well-distributed Pareto optimal solutions. The performance of the proposed algorithm successfully reflects the value of this paper.

The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation.

Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results.

Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement.

This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

This paper presents an interactive fuzzy satisfying method by assuming that the decision maker (DM) has fuzzy goals for each of the objective functions in multiobjective nonlinear programming problems. The fuzzy goals of the DM are quantified by eliciting the corresponding membership functions through the interaction with the DM. After determining the membership functions for each of the objective functions, in order to generate a candidate for the satisficing solution which is also a Pareto optimal, the DM selects an appropriate standing membership function and specifies his/her aspiration levels of achievement of the other membership functions, called constraint membership values. For the DM's constraint membership values, the corresponding constraint problem is solved and the DM is supplied with the Pareto optima] solution together with the trade‐off rates between a standing membership function and each of the other membership functions. Then by considering the current values of the membership functions as well as the trade‐off rates, the DM acts on this solution by updating his/her constraint membership values. In this way, the satisficing solution for the DM can be derived efficiently from among a Pareto optimal solution set by updating his/her constraint membership values. On the basis of the proposed method, a time‐sharing computer program is written and an application to regional planning is demonstrated along with the corresponding computer outputs.

The paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.

To optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.

This paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.

Very few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

There have been numerous efforts to tackle the problem of accumulated construction and demolition wastes worldwide. In this regard, this study develops a model for identifying the optimum fleet required for waste transportation. The proposed model is validated through a case study from the construction sector in New Cairo, Egypt.

Various fleet combinations are assessed against the time, cost, energy and emissions generated from waste transportation. Genetic algorithm optimization is performed to select the near-optimum solutions. Complex proportional assessment and operational competitiveness rating analysis decision-making techniques are applied to rank Pareto frontier solutions. These rankings are aggregated using an ensemble approach based on the half-quadratic theory. Finally, a sensitivity analysis is implemented to determine the most sensitive attribute.

The results reveal that the optimum fleet required for construction and demolition wastes (CDW) transportation consists of one wheel loader of bucket capacity 2.5 cubic meters and nine trucks of capacity 22 cubic meters. Furthermore, consensus index and trust level of 0.999 are obtained for the final ranking. This indicates that there is a high level of agreement between the rankings. Moreover, the most sensitive criterion (i.e. energy) is identified using a sensitivity analysis.

This study proposes an efficient and effective construction and demolition waste transportation strategy that will lead to economic gains and protect the environment. It aims to select the optimum fleet required for waste transportation based on economic, social and environmental aspects. The usefulness of this study is establishing a consensual decision through the aggregation of conflicting decision makers' preferences in waste transportation and management.

Argues that the high job mobility observed most prominently among workers in Japanese firms is consistent with the behaviour of risk‐averse individuals when neither private nor public income insurance is widely available to displaced workers. Laissez faire is suboptimal and involves higher job mobility than is socially optimal. Public provision of income insurance yields a Pareto improvement and reduces job rotation. Government job training schemes may push rotation levels even higher than the levels under laissez faire and could, therefore, be counterproductive.

The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit.

A cell division mechanism is introduced and used to design a new selecting mechanism in the proposed algorithm, a non-dominated sorting cellular genetic algorithm (NSCGA).

The numerical results on solving standard multi-objective test functions and a prototype MM unit positively demonstrate the advantages of the proposed NSCGA.

A new NSGAII-based optimization algorithm, NSCGA, for multi-objective optimization designs of a MM unit is proposed.

This paper aims to understand the aerodynamic blockage related to near casing flow in a transonic axial compressor using numerical simulations and to design an optimum casing groove for stall margin improvement using a surrogate optimisation technique.

A blockage parameter (Ψ) is introduced to quantify blockage across the blade domain. A surrogate optimisation technique is then used to find the optimum casing groove design that minimises blockage at an axial location where the blockage is maximum at near stall conditions.

An optimised casing groove that improves the stall margin by about 1% can be found through optimisation of the blockage parameter (Ψ).

Optimising for stall margin is rather lengthy and computationally expensive, as the stall margin of a compressor will only be known once a complete compressor map is constructed. This study shows that the cost of the optimisation can be reduced by using a suitably defined blockage parameter as the optimising parameter.

This paper describes an integrated structural optimization procedure in the vehicle structural concept design using multilevel decomposition technique. The structural responses in the lower level are represented in terms of local quantities that are the intermediate parameters or the detailed dimensions based on the parametric distributed screen from design and manufacturing constraints. And this technique is applied to develop the aluminum vehicle structure at concept design. We have the very efficiency results that decide the characteristics of master section and joint stiffness of vehicle structure. In the comparison with the base model the bending stiffness and torsional stiffness of the developed aluminum vehicle structure increase around 45% and 35% respectively, and the weight reduction is around 30%.