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This paper proposes a metaheuristic algorithm, called advanced differential evolution (ADE), by improving the DE algorithm. The ADE algorithm was developed with the goal of creating an optimization framework that addresses the challenges of exploration and exploitation balance, avoiding local minima, utilizing chaos theory for diverse initialization, and improving solution quality and convergence speed. By incorporating these features, ADE aims to enhance the effectiveness of optimization processes. The proposed algorithm utilizes chaos theory to generate the initial population, which is subsequently divided into two sub-populations with adaptive sizes. The size of each sub-population is determined using a formula based on the number of iterations during the algorithm's execution. The first sub-population has a larger size in the beginning and the second one has a smaller size, but the total size of these two populations is always constant. The main contribution of this paper is the proposal of two novel improved differential evolution algorithms, namely MDE1 and MDE2, which are utilized for exploration within these sub-populations. The proposed ADE is tested on 29 well-known benchmarks and six engineering problems, and the results are compared with seven other algorithms. Various statistical experiments are carried out showing that the proposed algorithm provides significant superiority over other well-known algorithms. [ABSTRACT FROM AUTHOR]

In many fields including aerospace, automotive, and telecommunications, MathWorks' MATLAB/Simulink is contemporary standard for model-based design. The strengths of Simulink are rapid design and algorithm exploration. Models created with Simulink are just functional. Therefore, designers cannot effortlessly consider a Simulink model's architecture. As current architectures are optimized to run on multicore processors, software running on these processors needs to be parallelized in order to benefit from their natural performance. For instance, designers need to understand how a Simulink model could be parallelized and how an adequate multicore architecture is selected. This paper focuses on the dataflow-based parallelization of Simulink models and proposes a method based on dataflow to measure the performance of parallelized Simulink models running on multicore architectures. Throughout the parallelization process, the model is converted into a Hierarchical Synchronous DataFlow Graph (HSDFG) keeping its original semantics, and each composite node in the graph is flattened. Then, the graph is mapped and scheduled into a multicore architecture with the ultimate objective that minimizes the end-to-end latency. In the experiment of applying the proposed approach to a real Simulink model, latency of the parallelized model could be reduced successfully on a various multi-core architectures. [ABSTRACT FROM AUTHOR]

The lemur optimizer (LO) is a new metaheuristic algorithm that is inspired by the two primary locomotive patterns of the lemurs. However, LO suffers from improper balance between exploration and exploitation phases and sub-optimal solution stagnation due to poor exploration capability. To address these limitations, an improved lemur optimizer (ILO) with dynamic scaling factors and Gaussian mutation is introduced in this paper. The dynamic scaling factors balance the exploration and exploitation transition by dynamically adjusting their values throughout the optimization process. The proposed algorithm incorporates Gaussian mutation to induce diversity in the population and hence improve the exploration capability. The efficiency of the proposed approach is tested on 33 functions including classical benchmark and CEC-2019 test functions, and a comprehensive comparison is made with six other well-known metaheuristic algorithms. The simulation and statistical results indicate superior performance of ILO in achieving optimal solutions compared to the competing algorithms. The algorithm is further employed to address two constrained classical engineering design problems, demonstrating its robustness. [ABSTRACT FROM AUTHOR]

Exact subgraph matching is fundamental to numerous graph data applications. The inherent NP-completeness of subgraph isomorphism poses significant challenges in developing efficient matching algorithms. Current methods show limited success, particularly in their filtering and verification stages. Selecting an optimal vertex-searching order can greatly improve a subgraph searching algorithm's effectiveness, yet a comprehensive theory for this selection is lacking. In this paper, we introduce the Multistage Graph Search Model (MGSM), a novel approach addressing this gap. MGSM provides insights for identifying the most efficient vertex-searching order and offers a systematic framework for evaluating existing algorithms. Using MGSM, we identify two main challenges in optimizing the search order and present a new matching algorithm "Tps" noted for its strategic vertex-searching order. Extensive experiments demonstrate the superior performance of Tps and its effectiveness and soundness in optimizing search orders. [ABSTRACT FROM AUTHOR]

Efficient Energy Consumption and Network Lifetime are significant concerns in wireless sensor networks and allied disciplines. Clustering is one of the available solutions, but optimized cluster head selection is a prime issue nowadays. Many solutions have been given for solving this issue considering some attributes, but with time, meta-heuristics algorithms have become widely used for real-world applications. The nature-inspired algorithm is seeking researchers' wide attention as it gives the capability to self-learn and perform better. In this paper, we have proposed the Dragon Fly algorithm with multi-attribute decision-making inspired by the dynamic and static behavior of the dragonfly. The Proposed IDA (Innovative Dragonfly Algorithm) is a hybrid approach in which dragonfly and multi-attributes are combined for optimal cluster head selection. The proposed method is a way to compute multi-attributes of sensor nodes for ranking them and selecting optimized cluster heads. The energy consumption of IDA is 0.4896, NBA (Novel Bio-Inspired Algorithm) is 0.4321, FLPSOC (Fuzzy Logic and PSO-based energy efficient clustering) is 0.4421, and ESO-LEACH (PSO-based energy efficiency) is 0.4678 at which means proposed IDA is better than other compared algorithms in energy consumption. The throughput of IDA is 64.99, which is better than existing different compared algorithms. The number of alive nodes in the proposed method is, and that of compared algorithms is; thus, IDA has enhanced network lifetime compared to others. The IDA algorithm is compared with NBA, FLPSOC, and ESO-LEACH, validating that the proposed algorithm performs better than the classical and compared algorithm. [ABSTRACT FROM AUTHOR]

Classic multidimensional scaling (MDS) and scaling by majorizing a complex function (SMACOF) are well-known centralized algorithms that are used to solve MDS problem. In this paper, we present a distributed algorithm for solving MDS problem. Estimations of coordinates are performed concurrently under the assumption that each item knows only its own position and its distances from its neighbors and their approximated present locations. The update process is done by calculating the average of the current coordinate of each object and its projections on the solution spaces allocated to it by its neighbors. We apply the method to the problem of sensor localization and obtain numerical results that demonstrate the efficacy of our suggested strategy. [ABSTRACT FROM AUTHOR]

Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is imperative to evaluate the predictive performance it attains for diverse datasets. This is done in order to stress test the strengths and weaknesses of the novel algorithms and provide evidence for situations in which they are most useful. A common practice is to gather some datasets from public benchmark repositories for such an evaluation. But little or no specific criteria are used in the selection of these datasets, which is often ad-hoc. In this paper, the importance of gathering a diverse benchmark of datasets in order to properly evaluate ML models and really understand their capabilities is investigated. Leveraging from meta-learning studies evaluating the diversity of public repositories of datasets, this paper introduces an optimization method to choose varied classification and regression datasets from a pool of candidate datasets. The method is based on maximum coverage, circular packing, and the meta-heuristic Lichtenberg Algorithm for ensuring that diverse datasets able to challenge the ML algorithms more broadly are chosen. The selections were compared experimentally with a random selection of datasets and with clustering by k-medoids and proved to be more effective regarding the diversity of the chosen benchmarks and the ability to challenge the ML algorithms at different levels. [ABSTRACT FROM AUTHOR]

The authors consider optimization transformations of the algorithm for constructing minimal generating sets of solutions of systems of linear homogeneous equations (SLHE) over the set of natural numbers. The paper describes the features of such SLHEs, substantiates optimization transformations, and presents examples of the algorithm before and after optimization transformations. It illustrates the applying the algorithm with examples of the analysis of Petri net properties and the construction of a basic solution set in the fields of complex, real, and rational numbers and over finite fields. [ABSTRACT FROM AUTHOR]

Tunicate Swarm Algorithm (TSA) is a new bio-based optimization technique that has proven not only to be able to compete with other methods but has also shown successful performance in classic design engineering problems/benchmark test problems. However, like some population-based methods, TSA tends to be trapped in local optima, converging to global optima in a long time, unbalanced exploitation/exploration, and the inability to effectively solve high-capacity engineering problems. In this paper, the M-TSA, which is a Modified version of the TSA, is proposed to overcome such problems. M-TSA was developed in three steps. The first is the new movement strategy that improves the movement of tunicates with a spiral movement, the second is the new herd strategy that improves the herd movement of tunics with the Levy movement, and the third is the consideration of the FAD effect. In this study, the efficiency and robustness of the M-TSA algorithm is tested on the CEC'17 test suite, six real-life design engineering problems, and two complex power system engineering problems. The test results were compared with other techniques reported in the literature and with the original TSA. Comparing the results from the M-TSA technique with other techniques proves the effectiveness of M-TSA with better exploration/exploitation balance and optimal solution finding. In this paper, MATLAB 2020b software is used for optimization problems simulation. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

In an Internet of Things (IoT) assisted Wireless Sensor Network (WSN), the location of the Base Station (BS) remains important. BS serves as the central hub for data collection, aggregation and communication within the network. Its location significantly impacts the overall network performance and energy efficiency. However, the role and requirements of the BS may vary depending on the specific implementation and integration of IoT assist opportunistic routing protocol. Therefore, this work propose a Meta-heuristic Based optimized location Allocation algorithm for baSE station in IoT assist WSNs (MBASE). The MBASE algorithm is based on Forwarder Node (FN) selection and using meta-heuristic based opportunistic routing. The FN is selected by a decision-making approach based on meta-heuristic search strategies which offers viable options for local and global optimization problems. This work aims to find a way to achieve a trade-off in this algorithm for efficient and appropriate placement of BS either at the centre or outside of the network deployment zone. Both positions of BS are optimization problems in the design of WSNs/IoTs. MBASE algorithm is proposed for identifying the optimized location allocation for BS and is based on metaheuristic. Simulation based analysis of essential operational and functional parameters such as Message Success Rate (MSR), Packet Delivery Ratio (PDR), Throughput, and Average energy consumption (AEC) is reported in the paper. Additionally, work compares BS locations using a common neighbourhood concept to assure fairness. The evaluation shows a significant improvements in the PDR, MSR, Throughput and AEC for two different BS locations. [ABSTRACT FROM AUTHOR]

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks. Low and discrete power and limited amount of memory and finite number of resources form some major bottlenecks in the workflow. Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility. Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms—grey wolf algorithm and firefly algorithm to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach based on energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for encryption of RGB color images by combining two encryption approaches: the spatial approach and the transformation approach. The proposed method uses the 3D fractional modified Henon map (3D FrMHM) and the discrete fractional Krawtchouk moments (FrDKM). We have also proposed a new hybrid optimization algorithm (H-SSAOA) to optimize the parameters of the proposed Henon map and the parameters of the Krawtchouk fractional moments. This algorithm is based on the hybridization of two metaheuristic algorithms: the "Salp Swarm Algorithm" (SSA) and the "Arithmetic Optimization Algorithm" (AOA). The simulation results reveal the optimization efficiency of the proposed hybrid algorithm H-SSAOA compared to other meta-heuristic algorithms and the efficiency of the suggested encryption method for encrypting RGB color images in terms of sensitivity to the security key and resistance to different attacks. [ABSTRACT FROM AUTHOR]

Wearable devices are a growing field of research that can have a wide range of applications. The energy harvester is the most common source of power for wearable devices as well as in wireless sensor networks that require a battery-free operation. However, their power is restricted; consequently, power saving is crucial for wearable devices. Finding the best schedule for the various tasks that run on the wearable device can help to reduce power consumption. This paper presents a task scheduler for wearable medical devices based on Gaining–Sharing Knowledge (GSK) algorithm. The purpose of this task scheduler is to handle the tasks of a heart rate sensor and a temperature sensor to optimize the energy consumption throughout wearable medical devices. The proposed GSK-based scheduling algorithm is assessed against the state-of-the-art technique. The data used in our experiments are collected from an in-lab prototype. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

The Cloud environment had been the go-to for many users recently. Once request from users get submitted, cloud resources are put into action to fulfill the request. Scheduling is the primary task in cloud that needs to be up-to the mark for completing the requests swiftly. Multiple dynamic requests are submitted simultaneously by cloud users that necessitates precise and prompt scheduling in cloud. Scheduling in cloud may be hampered by various constraints, take for example the various QoS parameters that needs to be upheld. Though many researchers had proposed solutions for scheduling in cloud, improvisations can still be made by combining several QoS parameters that help attain optimized scheduling in cloud to boost the overall cloud performance. In this paper, we had proposed a Binary Grey Wolf Optimization (BGWO) algorithm to optimize the scheduling activity in cloud computing environment. The BGWO is a multi-heuristic algorithm where tasks are scheduled based on a fitness function, explicitly designed for achieving optimization goal. The fitness function that had been designed comprises of three prime parameters namely, the degree of imbalance (DoI), energy consumption and makespan. The performance efficiency of the proposed BGWO had been ascertained by comparing it with Oppositional based Grey Wolf Optimization algorithm (OGWO) and Mean Grey Wolf Optimization algorithm (Mean GWO) with respect to imbalance, energy and makespan parameters. The proposed algorithm had produced a cumulative improvement of 10.13% and 17.4% for makespan, 30.18% and 41.96% for DoI, 8.94% and 14.95% for energy consumption parameters. Detailed comparative results obtained had been described in the Results part of this research article. [ABSTRACT FROM AUTHOR]

As one of recently proposed optimization algorithm, sine cosine algorithm (SCA) suffers from the problems of skipping over true solutions, stagnating in local optima and premature convergence. Therefore, an improved sine cosine algorithm with heterogeneous subpopulations (HSISCA) is developed in this paper. The population of HSISCA is divided into two subpopulations which comply with different search mechanisms and information sharing rules. One subpopulation is specified to enhance exploration by incorporating Levy flight and random search guidance into SCA, while the other is specified to enhance exploitation by adaptive differential mutation operator. The exploration subpopulation is denied access to the information of exploitation subpopulation, but the exploitation subpopulation is allowed access to the information of exploration subpopulation. Moreover, the greedy selection is applied to each individual to preserve useful information. HSISCA is tested on the CEC2014 and CEC2017 benchmark functions, and is used for designing fractional order PID (FOPID) controller. The results confirm the better performance of HSISCA compared to other competitive algorithms, and demonstrate the effectiveness of HSISCA in designing FOPID controller for complex systems. [ABSTRACT FROM AUTHOR]

An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lé vy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm's performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it's position updating process was slightly tweaked and took advantage of the chameleons' randomization as well as adopting several time-varying functions. Second, the Lévy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and exploitation conducts. The optimization performance of ECSA is shown by testing it on five various real ELD cases with a generator having 3, 13, 40, 80 and 140 units, each with different constraints. The results of the ELD systems' analysis depict that ECSA is better than the parent CSA and other state-of-the art methods. Further, the efficacy of ECSA was experimented on several benchmark test functions, and its performance was compared to other well-known optimization methods. Experimental results show that ECSA surpasses other methods on complex benchmark functions with modest computational burdens. The superiority and practicality of ECSA is demonstrated by getting new best solutions for large-scale ELD cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

In this paper, a new population initialization method for metaheuristic algorithms is proposed. In our approach, the initial set of candidate solutions is obtained through the sampling of the objective function with the use of the Metropolis–Hastings technique. Under this method, the set of initial solutions adopts a value close to the prominent values of the objective function to be optimized. Different from most of the initialization methods that consider only spatial distribution, in our algorithm, the initial points represent promising regions of the search space, which deserve to be exploited to identify the global optimum. These characteristics allow the proposed approach obtains a faster convergence and improves the quality of the produced solutions. With the objective to demonstrate the capacities of our initialization method, it has been embedded in the classical Differential Evolution algorithm. To evaluate its performance, the complete system has been tested in a set of representative benchmark functions extracted from several datasets. Experimental results show that the proposed technique presents a better convergence speed and an increment in the quality of the solutions than other similar approaches. [ABSTRACT FROM AUTHOR]

The multiverse optimizer (MVO) is one of the most trending algorithms used nowadays. The searching space in MVO is restricted by the best solution only, leading to a poor searching domain, therefore, a long searching time. This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method solves the problem of the average positioning (AP) by dynamically enhancing the equation of updating the AP based on the best and the second-best available solutions. To evaluate The proposed IMOMVO, several datasets scenarios containing various tasks and virtual machines (Vms) were used to test the approach's capability. Standard evaluation metrics are used to validate the results of the proposed method; task execution time, throughput, and the Vms processing power. The proposed method obtained better results according to the evaluation measures than other state-of-the-art methods. The execution time achieves less time when compared to the mMVO as the proposed method achieved 186.33 s for executing 100 tasks and 934.92 for executing 600 tasks. The throughput results also achieved astonishing results as for 100 tasks, the throughput achieved 0.19, and the Vm processing power for the proposed method was 0.25 Kw for executing 100 tasks. [ABSTRACT FROM AUTHOR]

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder–Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems. [ABSTRACT FROM AUTHOR]

High-dimensional Feature Selection Problems (HFSPs) have grown in popularity but remain challenging. When faced with such complex situations, the majority of currently employed Feature Selection (FS) methods for these problems drastically underperform in terms of effectiveness. To address HFSPs, a new Binary variant of the Ali Baba and the Forty Thieves (BAFT) algorithm known as binary adaptive elite opposition-based AFT (BAEOAFT), incorporating historical information and dimensional mutation is presented. The entire population is dynamically separated into two subpopulations in order to maintain population variety, and information and knowledge about individuals are extracted to offer adaptive and dynamic strategies in both subpopulations. Based on the individuals' history knowledge, Adaptive Tracking Distance (ATD) and Adaptive Perceptive Possibility (APP) schemes are presented for the exploration and exploitation subpopulations. A dynamic dimension mutation technique is used in the exploration subpopulation to enhance BAEOAFT's capacity in solving HFSPs. Meanwhile, the exploratory subpopulation uses Dlite Dynamic opposite Learning (EDL) to promote individual variety. Even if the exploitation group prematurely converges, the exploration subpopulation's variety can still be preserved. The proposed BAEOAFT-based FS technique was assessed by utilizing the k-nearest neighbor classifier on 20 HFSPs obtained from the UCI repository. The developed BAEOAFT achieved classification accuracy rates greater than those of its competitors and the conventional BAFT in more than 90% of the applied datasets. Additionally, BAEOAFT outperformed its rivals in terms of reduction rates while selecting the fewest number of features. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques. [ABSTRACT FROM AUTHOR]

In recent decades with the increase in the complexity of the problems, the need for high-performance and scalable optimization tools has been inevitable. Among different phenomena introduced to optimization problems, naturally inspired algorithms are favored. Also, encountering large-scale problems, high-performance tools like parallel implementations should be needed. In order to tackle this problem, the framework has been proposed that can wrap any swarm algorithm into an outperformer parallel and hybrid version. Six accepted swarm algorithms are selected to evaluate performance and compare the wrapped version with standard versions. Six nonlinear high-dimension benchmark functions are used to test the proposed algorithms. The experimental results show that wrapped versions outperform standard versions with the measurement of average best fitness. [ABSTRACT FROM AUTHOR]

An optimization algorithm is a step-by-step procedure which aims to achieve an optimum value (maximum or minimum) of an objective function. Several natural inspired meta-heuristic algorithms have been inspired to solve complex optimization problems by utilizing the potential advantages of swarm intelligence. In this paper, a new nature-inspired optimization algorithm which mimics the social hunting behavior of Red Piranha is developed, which is called Red Piranha Optimization (RPO). Although the piranha fish is famous for its extreme ferocity and thirst for blood, it sets the best examples of cooperation and organized teamwork, especially in the case of hunting or saving their eggs. The proposed RPO is established through three sequential phases, namely; (i) searching for a prey, (ii) encircling the prey, and (iii) attacking the prey. A mathematical model is provided for each phase of the proposed algorithm. RPO has salient properties such as; (i) it is very simple and easy to implement, (ii) it has a perfect ability to bypass local optima, and (iii) it can be employed for solving complex optimization problems covering different disciplines. To ensure the efficiency of the proposed RPO, it has been applied in feature selection, which is one of the important steps in solving the classification problem. Hence, recent bio-inspired optimization algorithms as well as the proposed RPO have been employed for selecting the most important features for diagnosing Covid-19. Experimental results have proven the effectiveness of the proposed RPO as it outperforms the recent bio-inspired optimization techniques according to accuracy, execution time, micro average precision, micro average recall, macro average precision, macro average recall, and f-measure calculations. [ABSTRACT FROM AUTHOR]

This paper proposes a simple and effective optimization algorithm named as "Improved Rao (I-Rao) algorithm". The I-Rao algorithm consists of two phases: the local exploitation phase and the global exploration phase. The local exploitation phase improves the exploitation of the search process of Rao algorithms and enhances the algorithm's convergence speed. The global exploration phase helps the algorithm to get away from the local optimum solutions and enhances the exploration ability of the search process of the algorithm. Like Rao algorithms, the I-Rao algorithm also does not need algorithm-specific control parameters. The effectiveness of the I-Rao algorithm is tested on 25 unconstrained benchmark functions, 45 complex CEC test functions, and 19 real-world constrained mechanical design optimization problems of CEC2020. The comparison of results reveals the effectiveness of the I-Rao algorithm over many state-of-the-art advanced optimization algorithms such as GWO, SCA, PSO, WOA, Jaya, IJAYA, MJAYA, PGJAYA, EJAYA, IUDE, εMAgES, iLSHADEε, and COLSHADE. The Friedman test is also carried out to check the overall performance of the I-Rao algorithm as compared to other advanced optimization algorithms considered. The convergence plots illustrate the convergence speed of the I-Rao algorithm. [ABSTRACT FROM AUTHOR]

Recently, substantial progress has been made in developing efficient algorithms for solving combinatorial optimization problems. In this paper, following this direction, the problem of designing supply chains which is an important combinatorial optimization problem is considered. The structural properties of supply chain models are investigated to transform such models into a generalized network optimization model. The transformation to a generalized network optimization model reduces the algorithms' solution time. Moreover, this paper proposes a new efficient hybrid algorithm based on tabu search and generalized network simplex algorithm (GN-TSA) for designing multi-objectives supply chain models. The developed algorithm's parameters are tuned properly, validated, and evaluated. The algorithm's performance is then compared to an exact algorithm embedded in the General Algebraic Modeling System (GAMS) and two metaheuristic algorithms, namely a linear programming simplex algorithm integrated with the tabu search approach (LP-TSA) and simulated annealing. The findings indicated that the GN-TSA obtains solutions very close to the exact algorithm with less computation time. In addition, the proposed algorithm outperforms the LP-TSA and simulated annealing in terms of computation time, while the quality of the solutions is the same as solutions obtained by LP-TSA and better than simulated annealing. On average, the results revealed that the reduction in computational time is more than 26.30% using GN-TSA compared to LP-TSA and simulated annealing. [ABSTRACT FROM AUTHOR]

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Several different setups may be introduced as candidate options in a process plan for manufacturing a workpiece. It is required to employ an efficient optimization method to decide on the most competent setups. In the existing methods for optimizing the process plan, the main problem is the large search space size, which reduces the speed of finding the optimal solution or causes getting caught in the local minima. To remedy this problem, an optimization method has been developed in the present study based on a combined approach by employing the simulated annealing and the permutation-based setup planning algorithms. A new cost function has been introduced in this approach. The proposed method also has other advantages and capabilities in line with minimizing the manufacturing error and obtaining a uniquely optimized solution. A comparative study has been conducted to illustrate the capability of the proposed method. The results indicate that the rate of convergence to the optimal solution increases and the performance in finding the solution improves by using the method described in this paper. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) have been used in many fields, and the localization technology is one of the core technologies of WSN. Distance Vector-Hop (DV-Hop) algorithm is one of the localization algorithms for WSN, which is widely used because of its simple principle and low cost. The traditional DV-Hop algorithm has high localization error, so the PMDDV-Hop algorithm is proposed in this paper. First, the average hop-size of anchor nodes is optimized by the Particle Swarm Optimization (PSO) algorithm to reduce the accumulation of errors. Then the coordinates of the unknown nodes are optimized using the Differential Evolutionary (DE) algorithm. To reduce the probability of falling into local optimum during evolution, the levy flight strategy is introduced into the DE algorithm to increase the diversity of the population. To further improve the performance of the PMDDV-Hop algorithm, the mutation factor and crossover factor in the DE algorithm are dynamically changed to make them adaptive to the degree of population evolution. Finally, extensive experimental simulations are conducted to evaluate the effectiveness of the PMDDV-Hop algorithm. Experimental results show that the PMDDV-Hop algorithm can effectively reduce the localization error. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization (TLBO) algorithm is a novel population-oriented meta-heuristic algorithm. In this paper, we introduce an improved teaching–learning-based algorithm (ITLBO) and combine it with numerical methods to solve some problems of nonlinear inverse partial differential equations. The basic TLBO algorithm has been enhance to increase its exploration and optimization capacities as well as diversity, increase the number of solutions and further converge to the appropriate solution by introducing the concept of grouping, elitism, elite group, elitism rate and the number of teachers. The most important advantage in implementing this scheme is that in addition to the fact that we have improved the TLBO algorithm with a new method, without guessing the type of answer function or the type of unknown function of the nonlinear inverse problems, we can determine the unknown and the answer of the inverse problems with great accuracy by optimizing the initial numerical values in a given interval. Furthermore, the experimental results on CEC benchmark function verify the feasibility and optimization performance of ITLBO. Accurate results were obtained by implementation proposed algorithms on 3.70 GHz clock speed CPU. [ABSTRACT FROM AUTHOR]

The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the publications. First, novel approaches contributing to the improvement of its performance. Second, solving the various optimization problems. Despite the different approaches and applications, there exists no work summarizing them. Therefore, this work reviews the research papers dealing with the principles and application of the SOMA. The second goal of this work is to provide additional information about the performance of the SOMA. This work presents the comparison of the selected algorithms. The experimental results indicate that the best-performing SOMAs provide competitive results comparing the recently published algorithms. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth literature review of the Black Hole Algorithm (BHA) which is considered as a recent metaheuristic. BHA has been proven to be very efficient in different applications. There has been several modifications and variants of this algorithm in the literature, so this work reviews various variants of the BHA. The applications of BHA in engineering problems, clustering, task scheduling, image processing, etc. have been thoroughly reviewed as well. This review article sheds lights on the pros and cons of this algorithm and enables finding a right variant of this algorithm for a certain application area. The paper concludes with an in-depth future direction. [ABSTRACT FROM AUTHOR]

One of the major problems of transport logistics is planning optimal delivery routes. Solving this problem leads to combinatorial optimizations that require complex computations. The present research considers an asymmetric problem of transport routing with a limitation of the carrying capacity of transport facilities, the duration of the route, and a heterogeneous transport facilities fleet. An algorithm for solving the routing problem based on the ant colony method is proposed. The web application that has been developed implements the proposed algorithm for solving this problem. The obtained optimal routes were compared with the results of route building by other cartographic services. The proposed algorithm showed the best result. [ABSTRACT FROM AUTHOR]

We study dense packings of a large number of congruent non-overlapping circles inside a square by looking for configurations which maximize the packing density, defined as the ratio between the area occupied by the disks and the area of the square container. The search for these configurations is carried out with the help of two algorithms that we have devised: a first algorithm is in charge of obtaining sufficiently dense configurations starting from a random guess, while a second algorithm improves the configurations obtained in the first stage. The algorithms can be used sequentially or independently. The performance of these algorithms is assessed by carrying out numerical tests for configurations with a large number of circles. [ABSTRACT FROM AUTHOR]

In this work, we are interested in the famous FISTA algorithm. We show that FISTA is an automatic geometrically optimized algorithm for functions satisfying a quadratic growth assumption. This explains why FISTA works better than the standard Forward-Backward algorithm (FB) in such a case, although FISTA is known to have a polynomial asymptotic convergence rate while FB is exponential. We provide a simple rule to tune the α parameter within the FISTA algorithm to reach an ε -solution with an optimal number of iterations. These new results highlight the efficiency of FISTA algorithms, and they rely on new non asymptotic bounds for FISTA. [ABSTRACT FROM AUTHOR]

Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR]

Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling. [ABSTRACT FROM AUTHOR]

This paper first reviews heuristic-based and bio-inspired contributions in inverse kinematics. A new inverse kinematics solver is then proposed based on beta distributed Salp Swarm Algorithm called β-SSA. The proposed algorithm is an alternative of the SSA algorithm where leading salps are distributed based on the beta function, enabling a better control of their repartition on the search space. The β -SSA inverse kinematics solver is named IK- β -SSA and can be considered as a generic framework. It uses a generic formulation of a forward kinematic model of a robotic system to retrieve its inverse solution. Inverse solution consists in obtaining a possible and feasible joint motions allow the robotic system to achieve a specific position while satisfying intrinsic constraints such as joints positions/ velocities limitations or path limitations. The β -SSA algorithm is first tested on a set of test functions and compared to nominal SSA prior to be applied to solve the inverse kinematics problem of the industrial robotic arm, Kuka Kr05-arc. The proposed method shows very competitive results when compared to classical SSA, QPSO, Bi-PSO, K-ABC and FA. The experimental results based on simulations and a Wilcoxon non-parametric statistical tests evidently show that the I K - β -SSA performs better than classical SSA, QPSO, Bi-PSO, K-ABC and FA for a single point inverse kinematics solution using a generic 8 Dof arm and the Kr05 industrial robot. For the path planning, a circular path tracking was investigated using the Kr05 robot and confirmed also that the β -SSA performs better than classical SSA, QPSO, Bi-PSO, K-ABC and FA. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA. [ABSTRACT FROM AUTHOR]

The weapon target assignment (WTA) problem is an important task to tactical arrangements in military commitment operations. It describes the optimal method to allocate defense in opposition to threats in fighting situations. It is an NP-complete issue in which no accurate outcome for all conceivable situations is known. The time performance of created algorithms is a major challenge in modeling the WTA problem, which has only been lately considered in related papers. This article improves the recently developed algorithm called improved Tunicate Swarm Algorithm (iTSA) which is inspired by the natural behavior of tunicates to solve the WTA problem. The suggested method is compared with well-known metaheuristic approaches. The experimental findings show that the method presented works better than previous competing metaheuristic approaches. [ABSTRACT FROM AUTHOR]

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, and traffic surveillance, etc. depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on good segmentation through multi-level thresholding. In this research, the algorithm includes two modules related to Entropy and variance. The first module is concerned with the modified firefly algorithm (FA) with Kapur's, Tsallis, and Fuzzy Entropy. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The second module is derived from the principle of variance between two classes known as between variance or inter-cluster variance. The opposition-based the learning method is used for initializing the population of candidate solutions and levying flight and local search is implemented with FA. The various experiments have been performed on Berkeley and benchmark images with distinct threshold (i.e. 2, 3, 4, 5) values. The proposed algorithm has been estimated and compared with known metaheuristic optimization methods like particle swarm optimization (PSO) and electromagnetism optimization (EMO). The results have been assessed quantitatively and qualitatively by using parameters like Peak signal-to-noise ratio (PSNR), structured similarity index metric (SSIM), objective function values, and convergence curve. The algorithm proposed observed better experiment results than PSO, EMO in terms of persistency and quality. [ABSTRACT FROM AUTHOR]

We introduce a novel gradient descent algorithm refining the well-known Gradient Sampling algorithm on the class of stratifiably smooth objective functions, which are defined as locally Lipschitz functions that are smooth on some regular pieces—called the strata—of the ambient Euclidean space. On this class of functions, our algorithm achieves a sub-linear convergence rate. We then apply our method to objective functions based on the (extended) persistent homology map computed over lower-star filters, which is a central tool of Topological Data Analysis. For this, we propose an efficient exploration of the corresponding stratification by using the Cayley graph of the permutation group. Finally, we provide benchmarks and novel topological optimization problems that demonstrate the utility and applicability of our framework. [ABSTRACT FROM AUTHOR]

Wireless communication technology is used in various applications and therefore the availability of wireless spectrum is a serious concern. The number of cellular users is increasing rapidly. The 5G network will be able to cater to the requirements of the increasing users. However, the spectrum efficiency needs to be improved. Cooperative spectrum sensing is being widely used by cognitive radios for utilizing the available spectrum in an efficient manner. Evolutionary Algorithm based optimization methods are used in various applications and have proved to be very efficient. These algorithms can also be used for optimizing the cooperative spectrum sensing in cognitive radios. In this paper, two methods are proposed for optimal Cooperative Spectrum Sensing for 5G cognitive networks. The optimization algorithms are designed using whale optimization algorithm (WOA) and Particle Swarm Optimization (PSO). The objective is to increase the probability of detection by optimizing the 'weighting vector'. In the first method, WOA is used for cooperative spectrum sensing optimization in cognitive radios. In the second method, WOA method is improved using the PSO algorithm. A hybridized WOA-PSO algorithm is proposed to further improve the probability of detection. The results obtained are compared with other existing algorithms. The proposed methods perform better than the existing methods. [ABSTRACT FROM AUTHOR]

The firefly algorithm is nature-inspired, and it belongs to swarm intelligence category optimization. Firefly algorithm is the latest algorithm developed under the nature-inspired algorithm (NIA), which is fruitful for business and engineering optimization. So, for business optimization, healthcare industries are affected through big data where big data affects in different ways such as patients understanding and care, improved personalized care, analysis trends, predict health outcomes, etc. Big data analysis is required to optimize a broad set of data generated by the different mediums in the healthcare and engineering sectors. A firefly algorithm can be used to optimize data analysis and results. Therefore, the firefly algorithm becomes essential to optimize the healthcare sector application process and provides optimized solutions. This paper discussed firefly algorithm implementations, optimization solutions for an engineering problem, fireflies' algorithm (FA) performance through MATLAB 2019b, formulation, optimization problems in healthcare, firefly algorithms in other applications, and conclusions. This paper focuses on how the firefly algorithm (FA) can be used, modified, and merged with other optimization algorithms to solve engineering problems. [ABSTRACT FROM AUTHOR]

This paper demonstrates an application of the improved crow search algorithm (I-CSA), which is a modified version of the crow search algorithm (CSA). CSA is a recent, nature-inspired meta-heuristic optimization algorithm. I-CSA differs from CSA by allowing application on a discrete problem, which is P-median and fortifying faster convergence to an optimal or near-optimal solution. Improvements are provided by local searches which support escaping from local optima or convergence to the optimal solution, elitism enhances the intensification through the utilization of nodes by selecting the most frequent centers that appeared in hiding better locations for local search, on the P-median problem. The application of the I-CSA is structured in three phases. In the first phase, parameters of I-CSA are analyzed and optimized using well-known data tests. The test datasets for the application of I-CSA on the P-median problem were retrieved from the OR-library to present the effectiveness and applicability of I-CSA. In the second phase, 40-pmed test problems from the library are solved using I-CSA and the results are compared with known optimal results and recorded results of other meta-heuristic approaches. In addition, Wilcoxon signed-rank test is applied in order to demonstrate the performance of I-CSA compared to well-known algorithms. The results of the proposed method demonstrated a faster convergence rate and better solution in most cases when compared with the standard CSA and other well-known meta-heuristic approaches. Finally, the proposed I-CSA approach is tested on a real-life case problem including 2121 nodes in Tunceli, Turkey. Obtaining the optimal results in a reasonable time indicates that the potential of the I-CSA is high and promising. In nutshell, this paper presents an improvement to CSA and evaluates its performance in a three-phase test procedure. [ABSTRACT FROM AUTHOR]

In this paper, the authors present a new algorithm efficient solution to the packing problem in two dimensions. The authors propose a new heuristic using the value of the electromagnetic field to determine the best position to place a circular object in a configuration of other circular objects previously packed. Also, this algorithm simulates two processes to compact objects already placed, inspired by gravitational forces, to minimize the empty space in the container and maximizing the number of objects in the container. To determine the efficacy of this algorithm, the authors carried out experiments with twenty-four instances. Parallel computing can contribute to making decision processes such as optimization and prediction more agile and faster. Real-time decision making involves the use of solution methodologies and algorithms. For this reason the present manuscript shows an alternative for the solution of a classic industry problem that must be solved quickly. Packaging optimization can help reduce waste of container material. The material used to transport the products can reduce its environmental impact due to an efficient packaging process. Light-weighting can also be accomplished by reducing the amount of packaging material used. [ABSTRACT FROM AUTHOR]