Competitive Swarm Optimization Algorithm Research Articles

An optimal power flow problem incorporating stochastic wind and solar power through the modified competitive swarm optimization algorithm

Increasing power demand day by day is the primary concern for power system practitioners and researchers. Generating a large amount of power through thermal power plants is neither economical nor environmentally friendly. The cost of fuel (Coal) is growing gradually as the coal reserves are depleting exponentially. Moreover, the government is imposing strict environmental guidelines on power-generating utilities. All these circumstances restrict the generation of electricity through fossil fuel-based plants. It is of most importance to develop for an alternative to fulfill consumers’ economic and clean energy demand. Based on it, this paper explains minimization fuel cost along with emission, power loss and voltage deviation. Modified competitive swarm optimization (MCSO) algorithm is used to determine optimization. The results obtained from the modified competitive swarm optimization algorithm are compared with the competitive swarm optimization (CSO) algorithm and successive history-based adaptive differential evolution (SHADE-SF) algorithm to verify the efficiency of modified competitive swarm optimization. The work is executed on a standard IEEE 30 bus system, and MATLAB is used as a simulation platform.

Competitive swarm optimization with subset selection based manifold learning for multimodal multi-objective optimization

There are two major challenges when handling the multimodal multi-objective optimization (MMO) problems. One is the loss of diversity since most of the evolutionary algorithms designed for MMO prefer the base algorithm with rapid convergence. Therefore, the extra niching or other diversity preserving mechanism is inevitable. The other is the distribution of the Pareto optimal solutions with imbalanced density. Since the Pareto optimal sets may show different characteristics in the decision space, it is tough to converge the solutions to the Pareto front uniformly. To address these issues, subset selection and manifold learning based competitive swarm optimization algorithm, namely MMO_CSO, is proposed. Competitive swarm optimizer which has well balance on both diversity and convergence is adopted. Moreover, a subset selection strategy is applied to select diversified individuals for learning the manifold structure of the Pareto set. Thereby, the subset selection based manifold learning mechanism is designed to generate the promising solutions which could approach the real Pareto solutions and fill the sparse Pareto subregion. Compared against six state-of-the-art peer algorithms, the proposed MMO_CSO has a better performance to search for the Pareto optimal solutions both in decision space and objective space on CEC2019 MMO benchmarks.

Adaptive multi-objective competitive swarm optimization algorithm based on kinematic analysis for municipal solid waste incineration

Multi-objective optimization for the municipal solid waste incineration process is considered as a valuable technique to improve energy recovery and reduce pollutant emission. However, the complex mechanism analysis and multimodal problem of the municipal solid waste incineration process set challenges for both the modeling and optimization studies. To overcome this problem, an adaptive multi-objective optimization for the municipal solid waste incineration process is proposed in this paper. First, a bi-objective model of the municipal solid waste incineration, the basis for optimization, is established based on mass balance and energy balance, which takes furnace temperature and flue gas oxygen content as decision variables to mathematically deduce the generated heat and exhaust gases. Second, an adaptive multi-objective competitive swarm optimization algorithm is proposed for the optimization of the municipal solid waste incineration process. Two-step competition and multi-strategy learning are designed to provide a clear division of labor for particles and a novel idea for detecting evolutionary environment is proposed based on kinematic analysis of particles. Finally, relevant experiments are conducted on benchmark instances and the municipal solid waste incineration optimization model. The proposed algorithm shows promising convergence, diversity, fastness by comparing with several representative and state-of-the-art algorithms. The proposed algorithm achieves the optimization effects with 4.36% improvement of the available heat for power generation and 4.13% reduction of the exhaust gas assessment in the optimization of the municipal solid waste incineration process.

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively.

Convex combination multiple populations competitive swarm optimization for moving target search using UAVs

Unmanned aerial vehicles (UAVs) searching for a moving target is a complex search optimization problem that aims to find a path with maximum probability of observing the target. However, as the area where the target may be located increases in size, existing methods tend to cause easy entrapment into local optima, resulting in poor performance. Hence, we propose a Convex Combination Multiple Populations Competitive Swarm Optimization algorithm (CDCSO) in this paper. First, we suggest applying the multiple population strategy to enhance the search ability of the algorithm. The population of interest is divided into two subgroups that each conduct the CSO. Second, we propose a novel convex combination update strategy to coordinate the two subgroups to search jointly for the global optimal solution. At each iteration, this strategy allows the two subgroups to compete and learn from each other, which prevents the population from falling into the local optimum. We then designed new scenarios containing 3 to 6 probability areas to investigate the performance of our proposed algorithm in multiple and complex scenarios.Results: To demonstrate the effectiveness and stability of the proposed algorithm, we compared it with several swarm optimization algorithms from different perspectives. Experimental results showed that the proposed algorithm has superior performance in following and intercepting a high-probability area. To further demonstrate the generality of the convex combination update strategy, we evaluated the strategy in several developed particle swarm optimization algorithms. The results demonstrated that the convex combination update strategy can be integrated into other algorithms to better address the moving target search problem.

Optimal infrastructure in microgrids with diverse uncertainties based on demand response, renewable energy sources and two-stage parallel optimization algorithm

Future electric grids will face severe uncertainties with the unprecedented penetration of renewable energy sources, which may cause problems in grid exploitation. It is essential to evaluate the uncertainty of system performance in this grid; therefore, traditional exploitation methods may not be suitable for distributions grid such as multi-carrier microgrids when considering the electricity and gas grid constraints. This paper proposes an effective method for simultaneously optimizing different types of energy infrastructure in an environment with various uncertainties while considering grid constraints. To address the multi-energy synergy supply optimization problem in the micro energy grid, a coordinated two-stage programming approach is also suggested. A day-ahead and real-time phases are separated in the scheduling cycle to overcome the effects of the unpredictability of wind energy and solar power. The findings of the day-ahead forecast are then taken into account as uncertain variables for the higher-layer model. The demand response programming model and the energy storage revision model are lower-layer models considering the actual value as the realization of uncertain variables. The competitive swarm optimization algorithm is used to solve the problem mentioned above The Cumulative search optimization (CSO) uses global and local systems in particle swarm optimization and adopts a novel learning mechanism for creating competition between particle pairs. Comprehensive numeric examinations show that convergence speed and precision are critical, especially in solving large-scale problems. Therefore, we use chaos theory to improve its performance. Finally, the proposed method is tested and discussed on a system. The results showed that: (1) power-to-gas could convert extra electricity into natural gas; (2) the proposed two-stage optimization model and algorithm achieved different energy optimizations; (3) price-based demand response (DR) can level the energy load curve and maximize multi energy grid (MEG) revenue by using complementary specifications of energy price; and (4) the micro energy grid could communicate with the high-degree energy grid in order to gain more economic benefits. Accordingly, the incentive-based demand response (IBDR) output is reduced. The electricity and heating provided by convention gas turbine (CGT) is increased, which is fed to upper power grid (UPG) and upper heating grid (UHG). Since MEG’s purchasing power from UPG during the peak period generates more total revenue than island operations. Based on the obtained results, it can be seen that the proposed algorithm was about 50% faster compared to other methods and its standard deviation was about 0.0013 with different implementations, which was much better compared to other models.

Large-Scale Competitive Learning-Based Salp Swarm for Global Optimization and Solving Constrained Mechanical and Engineering Design Problems

The Competitive Swarm Optimizer (CSO) has emerged as a prominent technique for solving intricate optimization problems by updating only half of the population in each iteration. Despite its effectiveness, the CSO algorithm often exhibits a slow convergence rate and a tendency to become trapped in local optimal solutions, as is common among metaheuristic algorithms. To address these challenges, this paper proposes a hybrid approach combining the CSO with the Salp Swarm algorithm (SSA), CL-SSA, to increase the convergence rate and enhance search space exploration. The proposed approach involves a two-step process. In the first step, a pairwise competition mechanism is introduced to segregate the solutions into winners and losers. The winning population is updated through strong exploitation using the SSA algorithm. In the second step, non-winning solutions learn from the winners, achieving a balance between exploration and exploitation. The performance of the CL-SSA is evaluated on various benchmark functions, including the CEC2017 benchmark with dimensions 50 and 100, the CEC2008lsgo benchmark with dimensions 200, 500 and 1000, as well as a set of seven well-known constrained design challenges in various engineering domains defined in the CEC2020 conference. The CL-SSA is compared to other metaheuristics and advanced algorithms, and its results are analyzed through statistical tests such as the Friedman and Wilcoxon rank-sum tests. The statistical analysis demonstrates that the CL-SSA algorithm exhibits improved exploitation, exploration, and convergence patterns compared to other algorithms, including SSA and CSO, as well as popular algorithms. Furthermore, the proposed hybrid approach performs better in solving most test functions.

An Enhanced Competitive Swarm Optimizer With Strongly Convex Sparse Operator for Large-Scale Multiobjective Optimization

Sparse multiobjective optimization problems (MOPs) have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensional feature selection. However, little attention has been paid to sparse large-scale MOPs, whose Pareto-optimal sets are sparse, i.e., with many decision variables equal to zero. To address this issue, this article proposes an enhanced competitive swarm optimization algorithm assisted by a strongly convex sparse operator (SCSparse). A tricompetition mechanism is introduced into competitive swarm optimization, aiming to strike a better balance between exploration and exploitation. In addition, the SCSparse is embedded in the position updating of the particles to generate sparse solutions. Our simulation results show that the proposed algorithm outperforms the state-of-the-art methods on both sparse test problems and application examples.

Multi-objective optimal control of bioconversion process considering system sensitivity and control variation

This paper aims to determine a time-varying dilution rate of the feed medium which maximizes mean productivity while minimizing both system sensitivity and control cost in glycerol continuous culture. A multi-objective optimal control problem subjected to a nonlinear control system and its auxiliary system is proposed and transformed into a finite-dimensional large-scale multi-objective optimization problem via discretization and time-scaling transformation. A multi-objective competitive swarm optimization algorithm is constructed to solve this transformed problem based on pairwise competition, mutation operation and environment selection. The Pareto front obtained shows the distribution of the optimal target value which indicates the rationality and feasibility of the control system under the approximate optimal strategy. Numerical simulations verify the robustness of the optimal control system and the effectiveness of our numerical solution method. The value of IGD shows the good accuracy and distribution of the proposed multi-objective optimization algorithm.

Combined economic emission dispatch in hybrid power systems using competitive swarm optimization

In last few decades, the emission of greenhouse gasses has exponentially increased due to large production of electric power energy from conventional fossil fuels to pose critical environmental challenges. The renewable energies (REs) are establishing themselves as key technologies for reduction of carbon emissions, in addition to low cost and high efficiency. However, the operational limits and the power generation procedures of the renewable energies invite immense challenges. The uncertainty in production with precise and error free approximation make it very complicated. Hence, an effective approach with methodical organization of the renewable energies are the need of the hour for reliable and safe system. In this study, an IEEE 30-bus hybrid power system (HPS) problem consisting of conventional thermal generators and green energies like wind generators and solar photovoltaic are considered to become environmentally and economically capable than the existing ones. Several measures like penalty cost and reserve cost have been considered in this present study for addressing the uncertainty issues underestimation and overestimation respectively. Further, three hybrid configurations such as thermal-solar (TS), thermal-wind (TW) and thermal-wind-solar (TWS) are proposed to perform the cost effective analysis. The adopted hybrid power system is extremely complex and non-linear optimization problem. Hence, a recently proposed evolutionary algorithm namely competitive swarm optimization (CSO) algorithm is implemented to discover the optimum result for the variety goals like minimum production cost, carbon emission, voltage variation and loss of the power. The performance of CSO algorithm is compared with several state-of-the-art meta-heuristic algorithms such GA, PSO, CSA, ABC, and SHADE-SF. The extraordinary outcomes achieved in this work illustrate that the CSO method can successfully be applied to handle the complex, non-convex and non-linear hybrid power system problems.

A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization

With the popularity of “flipped classrooms,” teachers pay more attention to cultivating students’ autonomous learning ability while imparting knowledge. Inspired by this, this paper proposes a Self-exploratory Competitive Swarm Optimization algorithm for Large-scale Multiobjective Optimization (SECSO). Its idea is very simple and there are no parameters that need to be adjusted. Particles evolve by exploring their neighboring space and learning from other particles in the swarm, thereby simultaneously enhancing the diversity and convergence performance of the algorithm. Compared with eight state-of-the-art large-scale multiobjective evolutionary algorithms, the proposed method exhibited outstanding performance on LSMOP problems with up to 10,000 decision variables. Unlike most existing large-scale evolutionary algorithms that usually require a large number of objective evaluations, SECSO shows the ability to find a set of well converged and diverse non-dominated solutions.

Learning Competitive Swarm Optimization.

Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

Solving static and dynamic multi-area economic dispatch problems using an improved competitive swarm optimization algorithm

Multi-area economic dispatch (MAED) is an important non-linear optimization problem in power system operation. MAED involves multiple power generation areas, and minimizes the total fuel cost by determining the power generation within areas and the power interchange between areas. This paper proposes an improved competitive swarm optimization (ImCSO) algorithm to solve the MAED problems. The ImCSO algorithm introduces two improvements into competitive swarm optimization for performance enhancement. Firstly, a ranking paired learning strategy is adopted to enhance the learning efficiency of the loser particles; secondly, a differential evolution strategy is used to update and improve the winner particles. Combining a constraint repair technique, the proposed ImCSO algorithm is applied to solve 10-unit, 40-unit and 120-unit multi-area static economic dispatch and 40-unit multi-area dynamic economic dispatch problems. The solved MAED problems integrate comprehensive constraints such as valve point effect, multiple fuels, transmission loss, tie-line constraint, prohibited operating zone and ramp rate limit. Through comparison with other well-established optimization algorithms, it is observed that the ImCSO algorithm has superiority in terms of solution accuracy and reliability in solving the MAED problems.

Improved Artificial Bee Colony Algorithm Based on Cauchy OBL

Artificial bee colony algorithm is a competitive swarm optimization algorithm for many optimization problems. However, traditional artificial bee colony algorithms often have the problems of insufficient development performance, easy to fall into local optimization and slow convergence. In order to overcome this defect, this paper introduces Cauchy OBL into artificial bee colony algorithm, and proposes an artificial bee colony algorithm based on Cauchy OBL. In the initialization stage, the algorithm uses the Cauchy OBL group to compete with the traditional initial group, selects excellent individuals to form an improved initial group, and improves the quality and diversity of the population; in the neighborhood search stage, the Cauchy OBL process is added to improve the global exploration ability of the algorithm. At the same time, in order to speed up the convergence speed, the global optimal solution and multi-dimensional update strategy are introduced. The simulation results show that the improved algorithm is easy to jump out of the local optimization and has higher search accuracy and faster convergence speed.

Performance analysis of unbalanced radial feeder for integrating energy storage system with wind generator using inherited competitive swarm optimization algorithm

In recent years, the battery energy storage system (BESS) has been considered as a promising solution for mitigating wind turbine generator intermittencies. In this paper, optimal location and capacity of the BESS with wind power penetrations, and charging/discharging dispatches of BESS are determined using inherited competitive swarm optimization (ICSO) algorithm while improving the performance of the unbalanced distribution network subject to technical constraints. The strategy uses the average load of the feeder as the deciding criterion for charging or discharging the battery. The sustainable average load (SAL) charging method is utilized to charge the battery. The free-running and conservative discharging methods of the battery are studied. The proposed methodology is investigated on IEEE 37 bus unbalanced radial distribution system. The application results show that the proposed methodology enhances various performance objectives of distribution system by optimally coordinating the dispatches of BESS.

Optimal capacitor placement and sizing in distribution system using Competitive Swarm Optimizer algorithm

Optimal capacitor placement and sizing in distribution system using Competitive Swarm Optimizer algorithm

An Asymmetric Chaotic Competitive Swarm Optimization Algorithm for Feature Selection in High-Dimensional Data

This paper presents a method for feature selection in a high-dimensional classification context. The proposed method finds a candidate solution based on quality criteria using subset searching. In this study, the competitive swarm optimization (CSO) algorithm was implemented to solve feature selection problems in high-dimensional data. A new asymmetric chaotic function was proposed and used to generate the population and search for a CSO solution. Its histogram is right-skewed. The proposed method is named an asymmetric chaotic competitive swarm optimization algorithm (ACCSO). According to the asymmetrical property of the proposed chaotic map, ACCSO prefers zero than one. Therefore, the solution is very compact and can achieve high classification accuracy with a minimal feature subset for high-dimensional datasets. The proposed method was evaluated on 12 datasets, with dimensions ranging from 4 to 10,304. ACCSO was compared to the original CSO algorithm and other metaheuristic algorithms. Experimental results show that the proposed method can increase accuracy and it reduces the number of selected features. Compared to different optimization algorithms with other wrappers, the proposed method exhibits excellent performance.

A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem

Large-scale multi-robot task allocation (MRTA) problem is an important part of intelligent logistics scheduling. And the load capacity of robot and picking station are important factors affecting the MRTA problem. In this paper, the MRTA problem is built as a many-objective optimization model with four objectives, which takes the load capacity of single robot, single picking station, all robots and all picking stations into account. To solve the model, a hybrid many-objective competitive swarm optimization (HMaCSO) algorithm is designed. The novel selection method employing two different measurement mechanisms will form the mating selection operation. Then the population will be updated by employing the competitive swarm optimization strategy. Meanwhile, the environment selection will play a role in choosing the excellent solution. To prove the superiority of our approach, there are two series of experiments are carried out. On the one hand, our approach is compared with other five famous many-objective algorithms on benchmark problem. On the other hand, the involved algorithms are applied in solving large-scale MRTA problem. Simulation results prove that the performance of our approach is superior than other algorithms.

A Novel Multi-Objective Competitive Swarm Optimization Algorithm

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study

The economic impact associated with power quality (PQ) problems in electrical systems is increasing, so PQ improvement research becomes a key task. In this paper, a Stockwell transform (ST)-based hybrid machine learning approach was used for the recognition and classification of power quality disturbances (PQDs). The ST of the PQDs was used to extract significant waveform features which constitute the input vectors for different machine learning approaches, including the K-nearest neighbors’ algorithm (K-NN), decision tree (DT), and support vector machine (SVM) used for classifying the PQDs. The procedure was optimized by using the genetic algorithm (GA) and the competitive swarm optimization algorithm (CSO). To test the proposed methodology, synthetic PQD waveforms were generated. Typical single disturbances for the voltage signal, as well as complex disturbances resulting from possible combinations of them, were considered. Furthermore, different levels of white Gaussian noise were added to the PQD waveforms while maintaining the desired accuracy level of the proposed classification methods. Finally, all the hybrid classification proposals were evaluated and the best one was compared with some others present in the literature. The proposed ST-based CSO-SVM method provides good results in terms of classification accuracy and noise immunity.