Exhaustive Search Method Research Articles

Power Allocation for IRS-Aided Two-Way Decode-and-Forward Relay Wireless Network

In this paper, an intelligent reflecting surface (IRS)-aided two-way decode-and-forward (DF) relay wireless network is considered, where two users exchange information via IRS and DF relay. To enhance the sum rate performance, three power allocation (PA) strategies are proposed. Firstly, a maximizing sum rate method based on successive convex approximation (Max-SR-SCA) is proposed to jointly optimize the PA factors of user1 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$U_{1}$</tex-math></inline-formula> ), user2 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$U_{2}$</tex-math></inline-formula> ) and relay station (RS). To further improve sum rate performance, a method of maximizing determinant (Max-Det) with a higher sum rate enhancement is presented. Considering the asymmetry of two-way rates caused by channel quality and two users' demand, maximizing sum rate with rate constraint (Max-SR-RC) is put forward. Simulation results show that the proposed three PA methods not only outperform the equal power allocation (EPA) method, but also the sum rates corresponding to the three PA methods are slightly lower than that of optimal exhaustive search (ES) method. Especially for Max-SR-RC method, its rate gain over EPA is up to 64.7% and its sum rate gap with optimal ES is less than 0.1bits/s/Hz. Furthermore, it is verified that the total power and random shadow variable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$X_\sigma$</tex-math></inline-formula> have a substantial impact on the sum rate.

Optimal Power Allocation With Multiple Joint Associations in Multi-User MIMO Full-Duplex Systems

Optimum power allocation is an effective way to mitigate residual self-interference and inter-user interference in multiple input multiple output full-duplex (FD) systems. However, current research mainly considers parts of influencing factors and sets service models fixed. Given this, we comprehensively focus on three perspectives in a novel power allocation method, which involve the muting management (MM) and the assignment of both base station antennas and subcarriers in the FD system. Then, we formulate an optimization problem to maximize the total spectrum efficiency. According to the categories of variables in the nonconvex objective function, we first propose a hierarchical algorithm, which is decomposed into the first-order Taylor approximation (FOTA) method and the greedy algorithm. The continuous and discrete variables related subproblems are solved through FOTA and greedy algorithm, respectively. Among them, the greedy algorithm is an alternative to a traditional method of exhaustive search. Considering the high complexity of the greedy algorithm, we further introduce deep reinforcement learning (DRL) instead to solve the corresponding subproblem. Thus, two Double Deep Q-learning Networks are constructed to train the samples in each sub-slot. Simulation results validate that the hybrid DRL-convex method outperforms the hybrid greedy-convex method. Meanwhile, the MM introduced scheme’s performance gain is more evident than that of the method without MM in many scenarios.

A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment

A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

Identifying the optimal heterogeneous ensemble learning model for building energy prediction using the exhaustive search method

Heterogeneous ensemble learning has received increasing attention in building energy prediction research because it provides more stable and accurate predictions than the conventional single model-based methods. Although existing studies have proved the feasibility of adopting heterogeneous ensemble learning in building energy prediction, they have not fully explored the method of constructing an optimal heterogeneous ensemble learning model based on limited base model resources. This paper attempts to identify the optimal heterogeneous ensemble learning model for building energy prediction by using the exhaustive search method. Six machine learning methods are used as the alternative base model for constructing heterogeneous ensemble learning models. All 57 heterogeneous ensemble learning models that can be built from the six base models are compared and the optimal model is identified. The hourly building energy prediction experiment is performed on an institutional building located at the University of Florida to verify the proposed exhaustive search method. Three modules representing different semesters of the test building are developed and tested to examine the predictive performance of the proposed heterogeneous ensemble learning models. The research findings demonstrate the feasibility of the proposed exhaustive search method in identifying the optimal heterogeneous ensemble learning model. The optimal heterogeneous ensemble learning model found in this study can reduce the MAPE by 4.9% − 6.6% compared with its most accurate base model in the model testing stage. The experimental results and research findings provide critical guidance for applying heterogeneous ensemble learning to data-driven building energy prediction.

Exhaustive Cross-Linking Search with Protein Feedback

Improving the sensitivity of protein-protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/β mass detection scheme and establishes protein-peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links.

Collaboration in the Sky: A Distributed Framework for Task Offloading and Resource Allocation in Multi-Access Edge Computing

Recently, unmanned aerial vehicles (UAVs)-assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates independently, however, it is challenging to meet the computation demands of the mobile users due to the limited computing capacity at the UAV’s MEC server as well as the UAV’s energy constraint. Therefore, collaboration among UAVs is needed. In this article, a collaborative multi-UAV-assisted MEC system integrated with an MEC-enabled terrestrial base station (BS) is proposed. Then, the problem of minimizing the total latency experienced by the mobile users in the proposed system is studied by optimizing the offloading decision as well as the allocation of communication and computing resources while satisfying the energy constraints of both mobile users and UAVs. The proposed problem is shown to be a nonconvex, mixed-integer nonlinear programming (MINLP) problem that is intractable. Therefore, the formulated problem is decomposed into three subproblems: 1) users tasks offloading decision problem; 2) communication resource allocation problem; and 3) UAV-assisted MEC decision problem. Then, the Lagrangian relaxation and alternating direction method of multipliers (ADMMs) methods are applied to solve the decomposed problems, alternatively. Simulation results show that the proposed approach reduces the average latency by up to 40.7% and 4.3% compared to the greedy and exhaustive search methods.

Searching for a compromise solution in cross-section size optimization problems of cold-formed steel structural members

A parametric optimization problem of cross-sectional sizes for cold-formed steel lipped channel structural members subjected to axial compression has been considered by the paper. An optimization problem is formulated as to define optimum cross-sectional sizes of cold-formed structural member taking into account post-buckling behavior (web and flange local and distortional buckling) of the member as well as structural requirements when the profile perimeter (strip width), profile thickness, design lengths of the structural member as well as material properties are constant and specified in advance. Maximization of the load-carrying capacity of the cold-formed structural member has been assumed as purpose function. The formulated parametric optimization problem has been solved by exhaustive search method using the software written in Python. As optimization results the cold-formed steel lipped channels with optimum cross-sectional dimensions have been obtained depending on the profile thickness and design lengths of the structural member. In order to obtain optimum solutions for cross-sectional dimensions of the CFS lipped channel structural members which are independent from the design flexural lengths and profile thickness, searching for a compromise solution has been performed by exhaustive search method. The obtained cold-formed steel lipped channel structural members with optimum cross-sectional sizes have higher design buckling resistance under the axial compression at the same material consumption (stripe width) comparing with the cold-formed steel lipped channels proposed by the manufacturer. Web local buckling phenomenon has been occurred in all obtained CFS lipped channel cross-sections with optimum sizes.

A novel hardware configuration method of contact R-test based on performance-constrained model

R-test is widely used to measure the rotary axis errors of five-axis machine tools due to its accuracy and convenience. There are some deficiencies in the research on the customized design of contact R-test under certain requirements of measuring range and accuracy. A novel hardware configuration method of contact R-test is proposed in this paper to realize customized configuration of hardware parameters of R-test. Firstly, a theoretical measurement model is established according to the structural characteristics of the contact R-test and is used as the evaluation model for measuring accuracy. Secondly, the influence of hardware parameters on the performance indexes is analyzed and the corresponding performance-constrained model is established. Thirdly, the configuration method of hardware parameters based on constraint models is proposed using the exhaustive search method and variable parameter method respectively. Finally, a prototype based on the configuration method is developed and calibrated on the Coordinate Measuring Machine (CMM), then the measuring performance evaluation and on-machine error measuring experiment are performed. The results show that the configuration method satisfies certain measuring requirements without range and precision waste. The proposed methods can guide the customized design of contact R-test.

Optimization of cross-section dimensions of structural members made of cold-formed profiles using compromise search

The object of this study is a structural member made from a C-shaped cold-formed profile, investigated to search for optimal cross-sectional dimensions. The parametric optimization task is stated as the problem to find the optimal cross-sectional dimensions of the structural member under axial compression conditions, taking into account its post-buckling behavior (local buckling of the web and flanges, as well as a distortional buckling of the cross-section) and structural requirements. In this case, the material consumption and mechanical characteristics of steel, as well as the design lengths of the structural member, were considered constant and predefined. The considered criterion of optimality was the maximization of the load-carrying capacity of the structural member for the overall buckling under the axial compression. The stated optimization problem is solved using the method of exhaustive search while applying the developed software. Additionally, for fixed steel consumption, compromise solutions were searched that do not depend on the thickness of the profile and the design lengths of the structural member. The resulting cold-formed C-shaped profiles with optimal cross-sectional dimensions are characterized by a higher load-carrying capacity for the overall buckling under axial compression (to 24.45 % and 22.19 %) at the same steel consumption compared to the profiles offered by the manufacturer. Analysis of the reported results made it possible to devise recommendations for optimal ratios of dimensions and geometric characteristics of the structural members made from C-shaped profiles operating under axial compression. The ratios could be used both at the stage of selection of cross-sections of structural members from cold-formed profiles, and in the development of effective assortments of cold-formed profiles

Extended logistic map for encryption of digital images

Abstract A novel extended logistic map has been proposed and tested mathematically for security-based applications. Because the designed extended logistic map behaves chaotically across a wide range of logistic control parameters, it is extremely difficult to predict using even the most exhaustive search methods. The map overcomes a significant drawback of simple logistic mapping, which is commonly used in encryption algorithms. The chaotic map designed was also used as a key to shuffle the pixel position of the image for the image shuffling algorithm developed. The algorithm developed produced excellent results and is adequate for providing an encrypted image in resource-constrained systems. Performance results show that this map is highly chaotic and provides high security when applied in image encryption systems.

Joint-Space CPG for Safe Foothold Planning and Body Pose Control During Locomotion and Climbing

From insects to larger mammals, legged animals can be seen easily traversing a wide variety of challenging environments, by carefully selecting, reaching, and exploiting high-quality contacts with the terrain. In contrast, existing robotic foothold planning methods remain computationally expensive, often relying on exhaustive search and/or (often offline) optimization methods, thus limiting their adoption for real-life robotic deployments. In this work, we propose a low-cost, bio-inspired foothold planning method for legged robots, which replicates the mechanism of the central nervous system of legged mammals. We develop a low-level joint-space CPG model along with a high-level vision-based controller that can inexpensively predict future foothold locations and locally optimize them based on a potential field based approach. Specifically, by reasoning about the quality of ground contacts and the robot's stability through the high-level vision-based controller, our CPG model smoothly and iteratively updates relevant locomotion parameters to both optimize foothold locations and body pose, directly in the joint space of the robot for easier implementation. We experimentally validate our control model on a modular hexapod on various locomotion tasks in obstacle-rich environments as well as on stair climbing. Our results show that our method enables stabler and steadier locomotion than an open-loop CPG controller, yielding higher-quality feedback from onboard sensors by minimizing the effect of slippage and unexpected impacts.

Position Design for Reconfigurable Intelligent-Surface-Aided Indoor Visible Light Communication Systems

As an emerging technology, reconfigurable intelligent surfaces (RISs) have been investigated to apply to visible light communication (VLC) systems to enhance the transmission capability of the systems, recently. However, the optimization of RIS location in VLC has not been studied. In this study, we first investigated RIS positioning design in VLC. Specifically, we set two indoor VLC scenarios with a VLC RIS composed of a mirror array. We set the achievable rates in different scenarios as the utility functions to optimize the position of the RIS array according to the placement of the access point (AP) and user. We found that the problems are nondeterministic polynomial (NP)-hard. Aiming at the different optimization problems, the particle swarm optimization (PSO) algorithm was used to confirm the optimal position of the RIS array. Unlike the traditional algorithm, we added an adaptive mutation mechanism to the algorithm to guarantee the randomness of the particle to search for the optimal solution. Finally, our simulation results showed that the proper position design of the RIS array can improve the communication performance to a greater degree, while the computational complexity required to solve the position optimization problems through the PSO algorithm can be significantly reduced compared with that required for the exhaustive search method in the case where both of the algorithms find the optimal solution.

A novel two-phase load compensation method under unbalanced voltages for renewable energy integration

This paper explores new design methods for correcting unbalanced power distribution networks in order to improve the power quality and reliability for sensitive industrial loads. While traditional compensators typically need to be connected to all the three phases of the AC network, this paper proposes a novel three-phase voltage balancing compensator that can operate with a connection to only two of the three phases. The new concept is based on the phasor symmetrical component theory, which can be implemented using either mechanically switched reactors (MSRs), TCRs (thyristor-controlled reactors) or VSCs (Voltage source converters). Three methods are presented to calculate the value of the proposed two-phase compensator, i.e., discrete solution, analytical solution and novel simplified solution. The discrete solution is solved via the exhaustive search method, which was successfully used by StruthersTech to correct the power quality of an unbalanced industrial power system. The analytical solution is based on the nodal voltage of analysis method. The simplified solution derives a practical albeit approximated relationship between the negative sequence source voltage and zero sequence load voltage, thus avoiding the need to solve nonlinear equations. Dynamic simulations are implemented using MATLAB/Simulink Simscape Blockset to validate the effectiveness of the proposed two-phase voltage balancing method.

SOC Estimation Using Deep Bidirectional Gated Recurrent Units With Tree Parzen Estimator Hyperparameter Optimization

State-of-charge (SOC) is a crucial battery quantity that needs constant monitoring to ensure cell longevity and safe operation. However, SOC is not an observable quantity and cannot be practically measured outside of laboratory environments. Hence, machine learning (ML) has been employed to map correlated observable signals such as voltage, current, and temperature to SOC values. In recent studies, deep learning (DL) has been a prominent ML approach outperforming many existing methods for SOC estimation. However, yielding optimal performance from DL models relies heavily on an appropriate selection of hyperparameters. At present, researchers relied on established heuristics to select hyperparameters through manual tuning or exhaustive search methods such as grid search and random search. This results in lengthy development time in addition to less accurate and inefficient models. This study proposes a systematic and automated approach to hyperparameter selection with a Bayesian optimization strategy known as tree Parzen estimator (TPE) in combination with the Hyperband pruning algorithm. The TPE optimization is run on various DL models such as BGRU, GRU, LSTM, CNN, FCN, and DNN to optimize for the best hyperparameter combination for each architecture. The Hyperband pruning algorithm is used to prune unpromising trials during the TPE run which results in time and computational resource savings. Experiment results show that the best performing model, BGRU-TPE incurs a low computation cost with no compromise in model accuracy, maintaining the best balance of both. The proposed model BGRU-TPE achieves the lowest RMSE and MAE error at 0.8% and 0.56%, respectively on various electric vehicle drive cycles at varying ambient temperatures and maintains a low computational cost at 15 600 FLOPS with the model size of only 193.3 KB.

Efficient enumeration-selection computational strategy for adaptive chemistry

Design problems of finding efficient patterns, adaptation of complex molecules to external environments, affinity of molecules to specific targets, dynamic adaptive behavior of chemical systems, reconstruction of 3D structures from diffraction data are examples of difficult to solve optimal design or inverse search problems. Nature inspires evolution strategies to solve design problems that are based on selection of successful adaptations and heritable traits over generations. To exploit this strategy in the creation of new materials, a concept of adaptive chemistry was proposed to provide a route for synthesis of self-adapting molecules that can fit to their environment. We propose a computational method of an efficient exhaustive search exploiting massive parallelization on modern GPUs, which finds a solution for an inverse problem by solving repetitively a direct problem in the mean field approximation. One example is the search for a composition of a copolymer that allows the polymer to translocate through a lipid membrane at a minimal time. Another example is a search of a copolymer sequence that maximizes the polymer load in the micelle defined by the radial core-shell potentials. The length and the composition of the sequence are adjusted to fit into the restricted environment. Hydrogen bonding is another pathway of adaptation to the environment through reversible links. A linear polymer that interacts with water through hydrogen bonds adjusts the position of hydrogen bonds along the chain as a function of the concentration field around monomers. In the last example, branching of the molecules is adjusted to external fields, providing molecules with annealed topology, that can be flexibly changed by changing external conditions. The method can be generalized and applied to a broad spectrum of design problems in chemistry and physics, where adaptive behavior in multi-parameter space in response to environmental conditions lead to non-trivial patterns or molecule architectures and compositions. It can further be combined with machine learning or other optimization techniques to explore more efficiently the parameter space.

Leveraging genetic algorithm to address multi-failure localization in optical networks

Fault management has long been an indispensable component for controlling and managing telecommunication networks. To prevent huge data losses, it is necessary to develop a fast and efficient fault localization mechanism. In this work, we study the problem of multi-failure localization in transparent optical networks. In this context, a correlation-based approach is introduced to exploit the quality of transmission of acquired lightpaths to localize the faulty links. The proposed search-based framework can be implemented by leveraging any search algorithm. One may utilize the exhaustive search method to localize faulty links more accurately, but at the cost of taking more time. On the other hand, one may utilize intelligent search methods with the aim of reducing the required time for localization at the expense of accuracy. However, we propose to use both of the search approaches together. In this way, faulty links are first localized by the intelligent search methods to reroute and restore the failed traffic as fast as possible to prevent further loss of data. To this aim, a genetic algorithm (GA) is proposed to search among the suspected links. Subsequently, exhaustive search method can be utilized to localize failures more accurately without time constraint and then send technicians to the right site to recover the faulty links. The obtained results reveal that the proposed GA approach achieves overall high localization accuracy (98.6%–100%) that is insignificantly affected as the traffic load decreases. Dual and triple-failure incidents are localized within 42–80 ms and 596–2180 ms, respectively. It is shown that the mean time required for localizing failures using the GA search algorithm is significantly lower than exhaustive search approach by several orders of magnitude. Hence, the proposed GA-based fault localization algorithm can reduce the average time required to restore the traffic in case of failures, applicable for the restoration applications.

A method for mixed additive and multiplicative random error models with inequality constraints in geodesy

In the geodetic data processing field, most methods for dealing with inequality constraints model are based on additive random error (ARE) models, and there have been few studies on mixed additive and multiplicative random error (MAAMRE) models with inequality constraints. To address this problem, a MAAMRE model with inequality constraints is first established based on the definition of inequality constraint equations, and then, a corresponding parameter estimation algorithm is proposed based on the idea of an exhaustive search method. In addition, considering a MAAMRE model for an ill-posed problem, an iterative regularization solution for an ill-posed MAAMRE model is first derived, and then, a specific parameter estimation algorithm for an ill-posed MAAMRE model with inequality constraints is further proposed by applying the exhaustive search approach. Finally, the feasibility and advantages of the proposed algorithms are verified by global positioning system (GPS) elevation fitting model and digital terrain model (DTM) examples.Graphical

On the Data Freshness for Industrial Internet of Things With Mobile-Edge Computing

This article studies the freshness of information with the aid of Age of Information (AoI) in the Industrial Internet of Things (IIoT), which plays a vital role to ensure quality and timely delivery of data services. To reduce the AoI, we leverage mobile-edge computing (MEC) to partially offload information to the mobile edge server. Aiming to cope with the packet error in the setting of short packet communication (SPC) in IIoT, we consider the standard automatic repeat request (ARQ) protocol with two policies, i.e., either retransmitting an out-of-date packet (RO) or transmitting a freshest packet (TF), when a packet error occurs. We derive the closed form of average AoI under these two policies, respectively, and then formulate the average AoI minimization problem by jointly optimizing the short packet blocklength and MEC offloading ratio. Due to the nonconvexity nature of the problem, we tackle it by employing block coordinate descent (BCD) and successive convex approximation (SCA) methods and then prove their convergence. Our extensive numerical results show that the optimal average AoI yielded by our proposed approach is almost identical to that from the high-complexity exhaustive search method, and has significant improvement over the benchmark methods. From the AoI perspective, it is revealed that the optimal strategy tends to offload all information to mobile edge server when the computing capacity of local device is less than a threshold. Furthermore, it is found that the RO policy is suitable for the relatively small bandwidth and large local computing capability scenario, whilst the TF policy is better for the large bandwidth and small local computing capability case.

Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers

We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let’s performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environments

Multi‐objective quantum atom search optimization algorithm for electric vehicle charging station planning

This paper presents an effective planning methodology for electric vehicle (EV) fast-charging stations (CS) using a multi-objective binary version of the atom search optimization (ASO) algorithm. The proposed method uses quantum operations to binarize the algorithm and achieve a higher convergence rate than the existing binary ASO algorithm. Additionally, a modified atom selection function is used to improve the searching capability of the ASO algorithm. Furthermore, the nondominated sorting procedure and pareto concepts are infused to solve the CS location problem (CSLP) considering the EV travel time, CS costs, and grid power loss as independent multi-objectives. The efficacy of the proposed multi-objective quantum ASO (MO-QASO) algorithm is evaluated using performance metrics namely, inverted generational distance (IGD), spacing (SP), and maximum spread (MS). The MO-QASO simulation results are compared with the results of other heuristic algorithms. MO-QASO achieves the best IGD (0.0021), SP (0.0002), and MS (0.9982) values, verifying the convergence and diversity of the algorithm. Importantly, the best CS planning solution obtained from MO-QASO is similar to the true solution obtained from the exhaustive search method. The MO-QASO efficiency is further validated by solving a CSLP from literature. Thus, the MO-QASO algorithm is a promising optimization tool for solving CSLP.