Multi-objective Bees Algorithm Research Articles

Optimisation of Product Recovery Options in End-of-Life Product Disassembly by Robots

In a circular economy, strategies for product recovery, such as reuse, recycling, and remanufacturing, play an important role at the end of a product’s life. A sustainability model was developed to solve the problem of sequence-dependent robotic disassembly line balancing. This research aimed to assess the viability of the model, which was optimised using the Multi-Objective Bees Algorithm in a robotic disassembly setting. Two industrial gear pumps were used as case studies. Four objectives (maximising profit, energy savings, emissions reductions and minimising line imbalance) were set. Several product recovery scenarios were developed to find the best recovery plans for each component. An efficient metaheuristic, the Bees Algorithm, was used to find the best solution. The robotic disassembly plans were generated and assigned to robotic workstations simultaneously. Using the proposed sustainability model on end-of-life industrial gear pumps shows the applicability of the model to real-world problems. The Multi-Objective Bees Algorithm was able to find the best scenario for product recovery by assigning each component to recycling, reuse, remanufacturing, or disposal. The performance of the algorithm is consistent, producing a similar performance for all sustainable strategies. This study addresses issues that arise with product recovery options for end-of-life products and provides optimal solutions through case studies.

A multiobjective decision-making approach for modelling and planning economically and environmentally sustainable robotic disassembly for remanufacturing

Adopting initiatives to extend the useful life of products or recovering their components for reuse, remanufacturing or recycling is a key issue in the attempt to protect the environment and minimise the demand for natural resources. To contribute to the performance of automated disassembly practices, this paper presents a multiobjective decision-making approach based on the optimisation of three goals in a robotic disassembly cell framework: enhancing the economic performance of the process, reducing energy consumption and mitigating the environmental impact. Two real-use cases are presented as demonstrators, supported by appropriate, updated information from industry. The design model allowed the authors to obtain the best robotic disassembly sequence plan, the correct disassembly direction, the best recovery option for the disassembled components – reuse, remanufacturing, recycling or disposal – and the most appropriate disassembly tools, finding the optimal or near-optimal solution that best balances the three sustainability goals. An Enhanced Discrete Bees Algorithm with a mutation operator was employed to find the solution for the optimisation. Moreover, a multiobjective Bees Algorithm, a Non-dominated Sorting Genetic Algorithm II and a Pareto Envelope-based Selection Algorithm II were adopted to solve the multiobjective optimisation problem using different iteration numbers and population sizes. The results provide insights into robotic disassembly processes, encouraging firms to adopt more automated and sustainable remanufacturing strategies.

Optimization of adhesive single-lap joints under bending moment

ABSTRACT This paper presents the optimization of stacking sequence of composite laminate adherends in an adhesive single lap joint under bending moment in order to minimize the amount of maximum peel and shear stress of the adhesive layer. The effects of different assumptions on the equations of the stress and their results are presented. The adhesive single lap joint is modelled as an adherend-adhesive sandwich panel. The finite element method is used to verify the analytical method and the structure is modelled by ANSYS software version 14. The results of the analytical method and the finite element method are compared, and there was a good agreement between two methods. For the optimization part, the single-objective and multi-objective bees algorithm are used to search for the best and worst stacking sequence with and without permutation. The results of the optimization indicate that the maximum shear and peel stress highly depend on the extensional, coupling and bending stiffness in a way that in the optimum model, adherends have high and almost equal stiffness. In the worst models, there is a huge difference in the stiffness of the adherends causing destructive high stress in the thinner adherend.

Analysis and application of inventive selective harmonic elimination strategy to eliminate high order harmonic from asymmetrical multi-level inverter

The most prominent issue relevant to the initiative multi-level inverters is how to provide a high-step staircase quasi-sinusoidal voltage against low switch numbers. This paper presents an innovative asymmetrical multi-level inverter structured by cascaded sub-multi-level inverter bonded to H-bridge inverter to unravel aforesaid issue. DC power sources of sub-multi-level inverters have been determined based on an especial exponential model to provide zero and positive steps, and so, negative steps can be achieved using H-bridge inverter bonded to cascaded sub-multi-level inverters. From viewpoint of harmonic components of high level output voltage, high order harmonics must be indispensability eliminated. Toward this subject, Rectangular Incision-Selective Harmonic Elimination-Pulse Width Modulation (RI-SHEPWM) along with fractional values of DC sources have been scheduled to be the fundamental approach aimed at elimination of twenty-ninth, thirty-first and thirty-fifth harmonics. In according to the nature of multi-objective state of the problem, executing the multi-objective optimization method isn’t avoidable. Due to high performance of Multi Objective Bees Algorithm (MOBA) to solve the non-linear objectives, the elimination of these harmonics is formulated by pertinent fitness functions utilizing MOBA. Putting it all together, the pertinent deductive analysis as well as both the simulation and experimental outcomes have transparently corroborated the capability of the held forth asymmetrical multi-level inverter. Meanwhile, elimination of all determined high-order harmonics i.e., twenty-ninth, thirty-first and thirty-fifth harmonics utilizing RI-SHEPWM based on MOBA has been approved.

MOBA based design of FOPID–SSSC for load frequency control of interconnected multi-area power systems

Automatic Generation Control (AGC) has functionally controlled the interchange power flow in order to suppress the dynamic oscillations of frequency and tie-line power deviations as a perturbation occurs in the interconnected multi-area power system. Furthermore, Flexible AC Transmission Systems (FACTS) can effectively assist AGC to more enhance the dynamic stability of power system. So, Static Synchronous Series Compensator (SSSC), one of the well-known FACTS devices, is here applied to accurately control and regulate the load frequency of multi-area multi-source interconnected power system. The research and efforts made in this regard have caused to introduce the Fractional Order Proportional Integral Derivative (FOPID) based SSSC, to alleviate both the most significant issues in multi-area interconnected power systems i.e., frequency and tie-line power deviations. Due to multi-objective nature of aforementioned problem, suppression of the frequency and tie-line power deviations is formularized in the form of a multi-object problem. Considering the high performance of Multi Objective Bees Algorithm (MOBA) in solution of the non-linear objectives, it has been utilized to appropriately unravel the optimization problem. To verify and validate the dynamic performance of self-defined FOPID–SSSC, it has been thoroughly evaluated in three different multi-area interconnected power systems. Meanwhile, the dynamic performance of FOPID–SSSC has been accurately compared with a conventional controller based SSSC while the power systems are affected by different Step Load Perturbations (SLPs). Eventually, the simulation results of all three power systems have transparently demonstrated the dynamic performance of FOPID–SSSC to significantly suppress the frequency and tie-line power deviations as compared to conventional controller based SSSC.

LVRT/HVRT capability enhancement of DFIG wind turbine using optimal design and control of novel PIλDμ-AMLI based DVR

Abstract Withstanding capability of Doubly Fed Induction Generator (DFIG) against the high voltage and low voltage is recognized as High Voltage Ride Through (HVRT) and Low Voltage Ride Through (LVRT). Dynamic Voltage Restorer (DVR), one of series Distributed-Flexible AC Transmission Systems (D-FACTS) families, has undertaken the DFIG perpetuation as any event to be taken place in the power system. Multi-Level Inverter (MLI) is the beating-heart of DVR with unique responsibility of synthesizing a staircase sinusoidal voltage from the DC voltage sources that ascertains the potentiality and cost-effectiveness of DVR. In this paper a novel asymmetrical MLI topology based on cascaded sub-MLI connected to full-bridge converter is indwelled in DVR so that its voltage quality to be ameliorated. To further consolidate DVR in dealing with HVRT and LVRT issues, it has been equipped with PI λ D μ controller based on Multi Objective Bees Algorithm (MOBA). The performance of suggested DVR-33 level AMLI-PI λ D μ -MOBA has been thoroughly evaluated under different severe and slight voltage sags and swells so that both the LVRT and HVRT capability of DFIG to be cleared up. To more scrutinize the performance of suggested DVR, it has been withal dealt with under flicker and harmonic voltage occurrences. Succinctly, the relevant analytical study along with the simulation results has transparently corroborated the performance of suggested AMLI as compared to other AMLIs. DVR-33 level AMLI-PI λ D μ -MOBA has eminently compensated different severe and slight voltage sags and swells as well as voltage flicker and voltage harmonic.

Maximum efficiency of wind energy using novel Dynamic Voltage Restorer for DFIG based Wind Turbine

Fault-tolerable capability of Doubly Fed Induction Generator (DFIG) against disturbance occurrence is recognized as Fault Ride-Through (FRT). With quick and accurate compensation potentiality of Dynamic Voltage Restorer (DVR) to retrieve the nominal PCC voltage, DFIG can safely follow its normal operation in the power system. Multi-Level Inverter (MLI) as the main part of DVR with unique responsibility of synthesizing the staircase sinusoidal voltage ascertains the performance and flexibility of DVR. In this paper a novel asymmetrical MLI structure based on Level Creator and H-bridge inverter is indwelt in DVR in order to providing the high quality voltage. To more augment the efficiency of DVR in dealing with FRT issue, it has been equipped with Brain Emotional Learning Based Intelligent Controller (BELBIC) based on Multi Objective Bees Algorithm (MOBA). So-called AMLI-BELBIC based DVR has been thoroughly appraised under different balanced and unbalanced voltage sags and swells so that FRT capability of DFIG to be appeared. To sum up, the relevant analytical expression along with the simulation results has transparently corroborated the performance of AMLI-BELBIC based DVR aimed to augment the FRT capability of DFIG.

A New Image Watermarking Scheme using Multi-Objective Bees Algorithm

The optimization of watermarked image fidelity and watermark robustness is an attractive research topic in image watermarking. In this paper, we propose a new image watermarking scheme based on multi-objective bees algorithm to promote watermarking robustness as well as fidelity. In contrast with the existing ge netic algorithms (GAs) based approaches utilizing single objective optimization, we treat the image watermarking as a multi-objective optimization problem. Based on this new perspective, the inherent conflict existing in image fidelity and watermark robustness fo r image watermarking can be objectively handled. The experimental study shows that our method indeed outperform the conventional GA-based approaches in both robustness and fidelity. Furthermore, the proposed watermarking method can also provide solutions with higher stability.

Multiobjective Bees Algorithm for Optimal Power Flow Problem

This paper presents a multiobjective bees algorithm (MOBA) for solving the multiobjective optimal power flow. The multiobjective optimal power flow is to simultaneously minimize total fuel cost and environmental pollution of generation with considering various constraints i.e. limits on generator real and reactive power outputs, bus voltages, transformer tap-setting and power flow of transmission lines. The proposed multiobjective bees algorithm is developed by using principle of multiobjective optimization. A clustering algorithm is applied for multiobjective bees algorithm in order to manage the size of the Pareto-optimal set. The proposed approach has been tested on the standard IEEE 30-bus system. The multiobjective bees algorithm produces true and well-distributed Pareto-optimal fronts in a single run. The results show that multiobjective bees algorithm has effectiveness and potential for solving multiobjective optimal power flow problem.

The Bees Algorithm with Weighted Sum Using Memorized Zones for Multi-objective Problem

This paper presents the newly developed Pareto-based multi-objective Bees Algorithm with weighted sum technique for solving a power system multi-objective nonlinear optimization problem. Specifically, the Pareto-based Bees Algorithm with memorized zone has been developed to alleviate both difficulties from classical techniques and intelligent techniques for multi-objective problems (MOP) and successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem. This multi-objective Bees Algorithm has been examined and applied to the standard IEEE 30-bus six-generator test system. Simulation results have been compared to those obtained using other approaches. The comparison shows the potential and effectiveness of the proposed Bees Algorithm for solving the multi-objective EED problem.