Multi-objective Meta-heuristic Algorithms Research Articles

Multi-objective metaheuristic search algorithms for service composition in IoT

The basic idea behind the Internet of Things (IoT) is to connect every physical Thing to the Internet. IoT adds ability in Things to sense and communicate with other Things. One of the main concerns in IoT is forming an optimal service composition to fulfil the user requirements while balancing the quality of service (QoS) parameters. So, in this paper, the service composition problem in IoT has been addressed using multi-objective optimisation. An optimal solution to this problem has been provided through proposed novel hybrid and proposed algorithm Multi-Objective Hybrid Hyper-Heuristic Flower Pollination Algorithm (MOHHFPA). The superiority of this algorithm is proved by empirically and statistically comparing it with existing multi-objective algorithms, namely the Non-dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Flower Pollination Algorithm (MOFPA), and the Multi-Objective Hyper-Heuristic Search Algorithm (MOHypEA). The proposed algorithm is empirically evaluated using a real-world case study.

Hybrid multi-objective metaheuristic algorithms for solving airline crew rostering problem with qualification and language.

In order to cope with the rapid growth of flights and limited crew members, the rational allocation of crew members is a strategy to greatly alleviate scarcity. However, if there is no appropriate allocation plan, some flights may be canceled because there is no pilot in the scheduling period. In this paper, we solved an airline crew rostering problem (CRP). We model the CRP as an integer programming model with multiple constraints and objectives. In this model, the schedule of pilots takes into account qualification restrictions and language restrictions, while maximizing the fairness and satisfaction of pilots. We propose the design of two hybrid metaheuristic algorithms based on a genetic algorithm, variable neighborhood search algorithm and the Aquila optimizer to face the trade-off between fairness and crew satisfaction. The simulation results show that our approach preserves the fairness of the system and maximizes the fairness at the cost of crew satisfaction.

Multi-Objective Optimization of 400 kV Composite Insulator Corona Ring Design

The electric field distribution is one of the main factors governing the long-term reliability of high voltage composite insulators. However, under severe pollution conditions, electric field stresses, when exceeding thresholds and applying for long periods, could lead to degradation and deterioration of the housing materials and, therefore, to failures of the composite insulators. This paper is intended to improve the distributions of the electric field and potential by minimizing the corona ring on a 400 kV AC transmission line composite insulator. The performances of three powerful multi-objective meta-heuristic algorithms, namely Ant Lion Optimizer (MOALO), Particle Swarm Optimizer (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II) are established to achieve this goal. First, variations of electrical fields on the critical parts of the string are obtained using three-dimensional finite element method (FEM) software. Then, three objective functions are developed to establish the relationships between the electric field and the guard ring parameters. Finally, the optimization parameters consist of diameter, tube diameter, and installation height of the corona ring. The obtained results confirm the effectiveness of the three algorithms; the MOLAO is the better in terms of computing time and solution quality.

Multi-objective equilibrium optimizer: framework and development for solving multi-objective optimization problems

ABSTRACT This paper proposes a new Multi-Objective Equilibrium Optimizer (MOEO) to handle complex optimization problems, including real-world engineering design optimization problems. The Equilibrium Optimizer (EO) is a recently reported physics-based metaheuristic algorithm, and it has been inspired by the models used to predict equilibrium state and dynamic state. A similar procedure is utilized in MOEO by combining models in a different target search space. The crowding distance mechanism is employed in the MOEO algorithm to balance exploitation and exploration phases as the search progresses. In addition, a non-dominated sorting strategy is also merged with the MOEO algorithm to preserve the population diversity and it has been considered as a crucial problem in multi-objective metaheuristic algorithms. An archive with an update function is used to uphold and improve the coverage of Pareto with optimal solutions. The performance of MOEO is validated for 33 contextual problems with 6 constrained, 12 unconstrained, and 15 practical constrained engineering design problems, including non-linear problems. The result obtained by the proposed MOEO algorithm is compared with other state-of-the-art multi-objective optimization algorithms. The quantitative and qualitative results indicate that the proposed MOEO provides more competitive outcomes than the different algorithms. From the results obtained for all 33 benchmark optimization problems, the efficiency, robustness, and exploration ability to solve multi-objective problems of the MOEO algorithm are well defined and clarified. The paper is further supported with extra online service and guideline at https://premkumarmanoharan.wixsite.com/mysite.

Hybrid meta-heuristic algorithms for optimising a sustainable agricultural supply chain network considering CO2 emissions and water consumption

In this research, a new mixed-integer linear programming (MILP) formulation for the production-distribution-routing problem is developed in a sustainable agricultural product supply chain network (SAPSCN) considering CO2 emission. The objective functions of the SAPSCN model seek to minimise the economic effects containing total cost in SAPSCN and environmental impacts including production and operation emissions, water consumption in production, operational water consumption, and transportation emission, as well as to maximise social impacts including on the number of the created works. Due to the complexity and NP-hardness of the SAPSCN formulation, four multi-objective meta-heuristic algorithms were applied, and two new hybrid meta-heuristic algorithms were developed. To assess the efficiency of the suggested meta-heuristic algorithms, various test instances were used to solve the proposed model and comparisons and sensitivity analyses were carried out with various criteria. A real case study is provided to validate the mathematical model. Finally, the results of the hybrid simulated annealing and particle swarm optimisation algorithm emphasises that it is more robust than other proposed algorithms to solve the problem in a reasonable time.

Designing an Agile Closed-Loop Supply Chain with Environmental Aspects Using a Novel Multiobjective Metaheuristic Algorithm

Success in supply chain implementation depends on the way of dealing with market changes and customer needs. Agility is a concept that has been introduced in recent years to improve the supply chain. On the other hand, paying attention to environmental problems is another issue, and chains are trying to increase their popularity by focusing on this issue. Considering the importance of this issue, designing a multiobjective closed-loop supply chain network has been discussed in this research. The main contribution of this research is the integration of green and agility concepts in supply chain design. In this regard, a mathematical model is presented with economic, environmental, and agility objectives. First, the mathematical model is solved using the Epsilon constraint method, and then, the multiobjective weed algorithm is proposed to solve the model. The results of comparisons between the two methods show that the multiobjective weed algorithm has performed well in terms of various metrics of NPS, SNS, and Max Spread. In terms of the solving time, the average solving time of this algorithm was about 0.1% of the solving time of the Epsilon constraint method. Moreover, all cases show the superiority of the multiobjective weed algorithm over the Epsilon constraint method in solving the proposed mathematical model.

A multi-objective optimization algorithm for forecasting the compressive strength of RAC with pozzolanic materials

Strengthened recycled aggregate concrete (RAC) with pozzolanic materials is considered eco-friendly concrete and the early assessment of its hardened characteristics is vital for the design and implementation procedures. Therefore, developing an accurate approach for modeling the compressive strength of strengthened RAC is essential since the compressive strength value is a necessary variable in different design codes. In this regard, a multi-objective metaheuristic (MSSA-DE) algorithm is proposed to forecast the compressive strength of the strengthened RAC at 28 days of curing age. This algorithm integrates the salp swarm algorithm (SSA) with the differential evolution technique (DE) via a multi-objective fitness function. To this end, the DE algorithm is used for enhancing the ability of feature exploitation in the SSA, where optimized parameters and structural learning are combined in the learning process to simultaneously boost the generalization performance. Then, a random population is produced, and the archive is generated. Finally, the current populations are updated and the solutions are determined. To study the effectiveness of the developed algorithm, a series of experimental and statistical tests are conducted. Results showed that the proposed MSSA-DE model is highly competitive and achieves better prediction performance compared with other metaheuristic models. A sensitivity analysis observed that the cement, water, and fine aggregate contents, as well as the physical properties of recycled aggregate, played a vital role in the model prediction.

Optimizing a bi-objective multi-period fish closed-loop supply chain network design by three multi-objective meta-heuristic algorithms

Attention to a food supply chain has increased recently due to population growth and increased demand for food. Aquaculture development is advantageous as fish is a crucial constituent of the food basket of households. This study first presents a new bi-objective and multi-period mathematical model of a fish closed-loop supply chain (CLSC). The model is addressed by utilizing the multi-objective Keshtel algorithm (MOKA), NSGA-II, and MOSA). The Taguchi method is employed to tune these meta-heuristics to attain superior performance, and the ε-constraint method is used in solving small-sized problems to validate them. The results show that the exact method cannot solve large-sized problems. The solutions are compared in terms of different performance metrics. Utilizing the ‘filtering/displaced ideal solution’ DIS method, NSGA-II and MOKA with a direct distance of 0.4228 and 0.8976 have the first and second performance ranks, respectively. Also, a case study including a trout CLSC in the north of Iran is investigated. The results and the case study show that the developed model can be applied to the proposed solution approach.

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.

Ground Structures-Based Topology Optimization of a Morphing Wing Using a Metaheuristic Algorithm

This paper presents multi-objective topology and sizing optimization of a morphing wing structure. The purpose of this paper is to design a new aircraft wing structure with a tapered shape for ribs, spars, and skins including a torsion beam for external actuating torques, which is anticipated to modify the aeroelastic characteristic of the aircraft wing using multi-objective optimization. Two multi-objective topology optimization problems are proposed employing ground element structures with high- and low-grid resolutions. The design problem is to minimize mass, maximize difference of lift effectiveness, and maximize the buckling factor of an aircraft wing subject to aeroelastic and structural constraints including lift effectiveness, critical speed, and buckling factors. The design variables include aircraft wing structure dimensions and thickness distribution. The proposed optimization problems are solved by an efficient multi-objective metaheuristic algorithm while the results are compared and discussed. The Pareto optimal fronts obtained for all tests were compared based on a hypervolume metric. The objective function values for Case I and Case II at 10 selected optimal solutions exhibit a range of structural mass as 115.3216–411.6250 kg, 125.0137–440.5869 kg, lift effectiveness as 1.0514–1.1451, 1.0834–1.1639 and bucking factor as 38.895–1133.1864 Hz, 158.1264–1844.4355 Hz, respectively. The best results reveal unconventional aircraft wing structures that can be manufactured using additive manufacturing. This research is expected to serve as a foundation for future research into multi-objective topology optimization of morphing wing structures based on the ground element framework.

Disaster relief supply chain design for personal protection equipment during the COVID-19 pandemic

The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters’ optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms’ applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.

Multi-objective metaheuristic algorithms for the mixed model assembly line sequencing problem with a bypass sub-line

ABSTRACT Nowadays, mixed-model assembly lines (MMALs) are extensively applied to manufacture different products with no need for the changeover of the whole lines to satisfy the diversified preferences of consumers. In some assembly lines, there is a considerable variation in cycle times, which reduces production efficiency. However, a bypass sub-line that undertakes a portion of product assembly operations reduces the variation in the assembly times. In the present paper, the following three objective functions are considered simultaneously: (1) minimizing the variation in the actual and required production capacity, (2) minimizing total utility work, and (3) minimizing total variation in production rate. The formulated sequencing problem is solved by two different Multi-Objective Evolutionary Algorithm (MOEAs) including the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). Then, some numerical examples are conducted, and the efficiency of the two proposed algorithms is measured based on some comparison metric.

Logistics planning of cash transfer to Syrian refugees in Turkey

This paper addresses a humanitarian logistics problem connected with the Syrian refugee crisis. The ongoing conflict in Syria has caused displacement of millions of people. Cash-based interventions play an important role in aiding people in the post-crisis period to enhance their well-being in the medium and longer term. The paper presents a study on how to design a network of administrative facilities to support the roll-out of cash-based interventions. The resulting multi-level network consists of a central registration facility, local temporary facilities, mobile facilities and vehicles for door-to-door visits. The goal is to reach the maximum number of eligible beneficiaries within a specified time period while minimizing logistics costs, subject to a limit on total security risk exposure. A mixed integer programming model is formulated to optimize the inter-related facility location and routing decisions under multiple objectives. The authors develop a hierarchical multi-objective metaheuristic algorithm to obtain efficient solutions. An application of the model and the solution algorithm to real data from a region in the southeast of Turkey is presented, with associated managerial insights.

Community Based Network Reconstruction for an Evolutionary Algorithm Framework

Inferring complex and non-linear dynamic system using the data that is available plays an important role in many areas of work such as physical, social, biological and computer sciences. In order to address these issues, network structure using a number of evolutionary algorithms has been proposed. However, the important criteria like the community structure have been ignored while developing these methodologies. Accordingly, this proposed work is focused on developing a multi-objective network reconstruction based on community structure in order to improve the network construction using ES by boosting their reconstruction performance. This framework that is used to further improve their performance is known as the community-based framework. It is based on multi-objective metaheuristic algorithm that is based on population and can be used as the base optimizer. The original decision space of the community structure is divided using the proposed work. From the solution obtained, an improved solution using reduced decision space is implemented using the multi-objective evolutionary algorithm (MOEA). A test suite is also designed to verify the performance of community based network reconstruction with respect to the complex network issue. In the proposed reconstruction methodology based on community criteria, the MOEAs are incorporated and are used to bind the original version. A noticeable improvement is seen in the experimental results based on the proposed work on 30 reconstruction issues.

A multi-objective parameter-tuned soft computing-based algorithm to optimize competitive congested location-pricing problem within multi-type service

One of the issues that has attracted many researchers in the last decade is the problem of locating facilities i.e. hospitals, shops, banks and ATMs. One of the basic needs of the people of the community is easy access to the facilities, so that by spending little time can reach to the facility and with spending low cost to receive their facilities. In this paper, the location-pricing problem of the congested facilities by considering competition between available facilities and new facilities were investigated and bi-objective non-linear mathematical model that follow from M/M/m/k queuing system, was presented. In the first goal, maximize the profit of system by minimizing the total cost of establishing the facilities, shipping costumers and expectation time of the costumers in the queue and in the second goal the share of facility market minimized. The proposed model is in the category of non-linear integer programming problems, that, due to the complexity of the problem in the large scales and in order to solve the model, different approaches such as multi-objective meta-heuristic algorithms including non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) has been presented. At the end, by applying the Taguchi method, the efficiency performance of NSGA-II algorithm perform better than MOPSO.

Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimisation algorithm

ABSTRACT CO2 emission reduction in large-scale pavement network maintenance planning has been an immense concern. The conventional single-objective optimisation modelsoverlook environmental issues such as CO2 emission. However, the introduced multi-objective optimisation aims to enhance the network condition and minimise CO2 emissions simultaneously. Two single-objective (coyote optimisation algorithm and genetic algorithm) and two multi-objective metaheuristic algorithms (multi-objective coyote optimisation algorithm and non-dominated sorting genetic algorithm) are employed to assess the effectiveness of the introduced environmental approach. Pavement maintenance planning optimisation requires the deterioration function formula and treatment improvement equation to be modelled. Hence, a new machine learning method called ‘differential evolutionary programming’ is introduced, which can provide the output-input formula. Differential evolutionary programming predicts the pavement deterioration value and overlay improvement with R2 of 0.992 and 0.970, respectively. The results indicate that the coyote optimisation algorithm’s objective function is 66% lower than that of the genetic algorithm. Likewise, the multi-objective coyote optimisation algorithm reduces the first objective function by 72% on average compared to the non-dominated sorting genetic algorithm. The grey relational analysis is performed to compare single-objective and multi-objective optimal solutions. All optimal solutions presented by multi-objective modelling dominates the single-objective optimisation optimal solution based on the grey relational grade.

On the Use of Multi-Objective Metaheuristic Optimization Algorithms to Increase the Classification Performance of Images Produced from Hybrid Pansharpening Methods

Görüntü sınıflandırma uygulamaları yüksek renk kalitesine sahip olan görüntülere ihtiyaç duymaktadır. Ancak, gerek algılayıcılardaki teknik kısıtlamalar, gerekse de maliyet problemlerinden dolayı bu tarz görüntüleri elde etmek her zaman mümkün olmamaktadır. Bu nedenle, bu çalışmada, literatürde ilk defa, çok amaçlı bir metasezgisel optimizasyon algoritması olan Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algoritması, hibrit bir pan-keskinleştirme yöntemi olan Intensity-Hue-Saturation Discrete Wavelet Transform (IHS-DWT) yönteminin performansının iyileştirilmesi amacıyla kullanılmıştır. Önerilen yöntem ile renk kalitesi en iyiden en kötüye değişen beş farklı pan-keskinleştirilmiş görüntü üretilmiştir. Pan-keskinleştirmede kullanılan girdi Çok Bantlı (ÇB) görüntü ile üretilen bütün pan-keskinleştirilmiş görüntüler Yapay Sinir Ağları (YSA) yöntemine göre sınıflandırılmıştır. Sınıflandırma sonuçları, önerilen yöntem ile üretilen bütün pan-keskinleştirilmiş görüntülerin toplam sınıflandırma doğruluğunu belli oranda arttırdığını göstermektedir. Ayrıca, önerilen yöntem ile üretilen en iyi renk kalitesine sahip olan iki görüntünün toplam sınıflandırma doğruluğunu yaklaşık % 24 oranında arttırdığı tespit edilmiştir. Önerilen yöntem ile yüksek sınıflandırma doğruluğuna ihtiyaç duyulan uygulamalar için altlık üretilebileceği sonucuna varılmıştır.

Identification of significant bio-markers from high-dimensional cancerous data employing a modified multi-objective meta-heuristic algorithm

Identification of the most prominent bio-markers or genes with high classification accuracy in the high-dimensional cancerous data, is still remained as an emerging challenge for the machine learning researchers. As this challenge has two objectives, i.e. minimizing the number of genes (NoG) and maximizing the classification accuracy percentage (CAP), this problem can be modeled as binary multi-objective approach. In this work, a modified version of multi-objective Jaya algorithm, multi-objective chaotic Jaya (MOCJaya), is suggested to select the minimum NoG with high CAP. Initially, a filter approach, namely Fisher score is applied to pre-select the informative genes. Then, MOCJaya algorithm is employed for both selecting key genes and classifying the cancer data. To assess the efficacy of the designed algorithm, ten binary and multi-class cancerous datasets are considered. Here, the suggested algorithm has been compared with multi-objective chaotic Genetic Algorithm (MOCGA), multi-objective chaotic particle swarm optimization (MOCPSO), multi-objective Jaya (MOJaya), multi-objective PSO (MOPSO), and non-dominated sorting GA (NSGA-II) models. Moreover, a comparison of MOCJaya algorithm with other seventeen existing models, is also performed here. The experimental results and comparison analysis reveal that MOCJaya classifies both the positive and negative samples of the cancer datasets in high CAP with a smaller NoG.

Spiral water cycle algorithm for solving multi-objective optimization and truss optimization problems

This paper addresses multi-objective optimization and the truss optimization problem employing a novel meta-heuristic that is based on the real-world water cycle behavior in rivers, rainfalls, streams, etc. This meta-heuristic is called multi-objective water cycle algorithm (MOWCA) which is receiving great attention from researchers due to the good performance in handling optimization problems in different fields. Additionally, the hyperbolic spiral movement is integrated into the basic MOWCA to guide the agents throughout the search space. Consequently, under this hyperbolic spiral movement, the exploitation ability of the proposed MOSWCA is promoted. To assess the robustness and coherence of the MOSWCA, the performance of the proposed MOSWCA is analysed on some multi-objective optimisation benchmark functions; and three truss structure optimization problems. The results obtained by the MOSWCA of all test problems were compared with various multi-objective meta-heuristic algorithms reported in the literature. From the empirical results, it is evident that the suggested approach reaches an excellent performance when solving multi-objective optimization and the truss optimization problems.

MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems

This paper proposes a new Multi-Objective Plasma Generation Optimization (MOPGO) algorithm, and its non-dominated sorting mechanism is investigated for numerous challenging real-world structural optimization design problems. The Plasma Generation Optimization (PGO) algorithm is a recently reported physics-based algorithm inspired by the generation process of plasma in which electron movement and its energy level are based on excitation modes, de-excitation, and ionization processes. As the search progresses, a better balance between exploration and exploitation has a more significant impact on the results; thus, the crowding distance feature is incorporated in the proposed MOPGO algorithm. Also, the proposed posteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is a crucial problem in multi-objective meta-heuristic algorithms. In truss design problems, minimization of the truss's mass and maximization of nodal displacement are considered objective functions. In contrast, elemental stress and discrete cross-sectional areas are assumed to be behavior and side constraints, respectively. The usefulness of MOPGO to solve complex problems is validated by eight truss-bar design problems. The efficacy of MOPGO is evaluated based on ten performance metrics. The results demonstrate that the proposed MOPGO algorithm achieves the optimal solution with less computational complexity and has a better convergence, coverage, diversity, and spread. The Pareto fronts of MOPGO are compared and contrasted with multi-objective passing vehicle search algorithm, multi-objective slime mould algorithm, multi-objective symbiotic organisms search algorithm, and multi-objective ant lion optimization algorithm. This study will be further supported with external guidance at https://premkumarmanoharan.wixsite.com/mysite.