Competitive Swarm Optimizer Research Articles

Learning-guided cross-sampling for large-scale evolutionary multi-objective optimization

When tackling large-scale multi-objective problems (LSMOPs), the computational budget could be wasted by traditional offspring generators that explore the search space in a nearly directionless manner, impairing the efficiency of many existing algorithms. To address this issue, this paper proposes a novel two-level large-scale multi-objective evolutionary algorithm called LMOEA-LGCS that incorporates neural network (NN) learning-guided cross-sampling for offspring generation in the first level and a layered competitive swarm optimizer in the second level. Specifically, in the first level, two NNs are trained online to learn promising vertical and horizontal search directions, respectively, against the Pareto Set, and then a batch of candidate solutions are sampled on the learned directions. The merit of learning two explicit search directions is to devote the employed NNs to concentrating on separate or even conflicting targets, i.e., the convergence and diversity of the population, thus achieving a good trade-off between them. In this way, the algorithm can thus explore adaptively towards more promising search directions that have the potential to facilitate the convergence of the population while maintaining a good diversity. In the second level, the layered competitive swarm optimizer is employed to perform a deeper optimization of the solutions generated in the first level across the entire search space to increase their diversity further. Comparisons with six state-of-the-art algorithms on three LSMOP benchmarks, i.e., the LSMOP, UF, and IMF, with 2-12 objectives and 500-8000 decision variables, and the real-world problem TREE demonstrate the advantages of the proposed algorithm.

An enhanced competitive swarm optimizer with strongly robust sparse operator for large-scale sparse multi-objective optimization problem

In the real world, the decision variables of large-scale sparse multi-objective problems are high-dimensional, and most Pareto optimal solutions are sparse. The balance of the algorithms is difficult to control, so it is challenging to deal with such problems in general. Therefore, An Enhanced Competitive Swarm Optimizer with Strongly Robust Sparse Operator (SR-ECSO) algorithm is proposed. Firstly, the strongly robust sparse functions which accelerate particles in the population better sparsity in decision space, are used in high-dimensional decision variables. Secondly, the diversity of sparse solutions is maintained, and the convergence balance of the algorithm is enhanced by the introduction of an adaptive random perturbation operator. Finally, the state of the particles is updated using a swarm optimizer to improve population competitiveness. To verify the proposed algorithm, we tested eight large-scale sparse benchmark problems, and the decision variables were set in three groups with 100, 500, and 1000 as examples. Experimental results show that the algorithm is promising for solving large-scale sparse optimization problems.

Constrained large-scale multiobjective optimization based on a competitive and cooperative swarm optimizer

Many engineering application problems can be modeled as constrained multiobjective optimization problems (CMOPs), which have attracted much attention. In solving CMOPs, existing algorithms encounter difficulties in balancing conflicting objectives and constraints. Worse still, the performance of the algorithms deteriorates drastically when the size of the decision variables scales up. To address these issues, this study proposes a competitive and cooperative swarm optimizer for large-scale CMOPs. To balance conflict objectives and constraints, a bidirectional search mechanism based on competitive and cooperative swarms is designed. It involves two swarms, approximating the true Pareto front from two directions. To enhance the search efficiency in large-scale space, we propose a fast-converging competitive swarm optimizer. Unlike existing competitive swarm optimizers, the proposed optimizer updates the velocity and position of all particles at each iteration. Additionally, to reduce the search range of the decision space, a fuzzy decision variables operator is used. Comparison experiments have been performed on test instances with 100–1000 decision variables. Experiments demonstrate the superior performance of the proposed algorithm over five peer algorithms.

Competitive swarm optimizer with dynamic multi-competitions and convergence accelerator for large-scale optimization problems

Large-scale optimizations (LSOPs) with high dimensional decision variables have become one of the most challenging problems in engineering optimization. High dimensional information causes serious interference to the algorithm optimization performance. The optimization performance of the algorithms will be seriously degraded. Competitive swarm optimizer (CSO) is a robust algorithm to tackle LSOPs. However, CSO randomly selects two particles to compare, then generates the winner and the loser. Although this search mechanism can enhance the diversity of the swarm, a single comparison is difficult to guarantee the quality of winners and losers. Therefore, there exists a risk of producing unqualified solutions. In order to enhance the quality of solution, a novel CSO with dynamic multi-competitions and convergence accelerator, namely DMCACSO, is designed in this paper. In the DMCACSO, a dynamic multi-competitions based evolutionary information is designed to pick out the losers more efficiently and improve the quality of winners. In addition, a convergence accelerator with hybrid evolutionary strategy is developed to speed up the particle search when the algorithm is a state of stagnation. The experiment results in solving large-scale benchmark functions from CEC2010 and CEC2013 indicate that the DMCACSO has competitive optimization performance by comparing with some state-of-the-art algorithms. Finally, the DMCACSO is effective in terms of quality in solving an actual feature selection problem.

A day‐ahead energy management strategy for electric vehicles in parking lots considering multi‐scenario simulations and hydrogen storage system

AbstractThis article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi‐scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large‐scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33‐bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid.

Optimal designs for nonlinear mixed-effects models using competitive swarm optimizer with mutated agents

Optimal designs for nonlinear mixed-effects models using competitive swarm optimizer with mutated agents

A switching competitive swarm optimizer for multi-objective optimization with irregular Pareto fronts

In the past two decades, multi-objective particle swarm optimization, as a powerful swarm intelligence paradigm, has been widely used to solve multi-objective optimization problems. By further exploring the competition mechanism among particles in the swarm, some particle update strategies have been proposed, which are effective in improving convergence performance and search speed. However, these strategies encounter difficulties in properly approximating irregular Pareto fronts (PFs), as their geometric structures are complex, leading to get trapped in local optima. To address this issue, a switching competitive swarm optimizer for multi-objective optimization with irregular PFs is proposed. In comparison with existing approaches that utilize reference vectors to guide the search or selection process, the proposed approach introduces a competition-based learning strategy with a novel winner determination mechanism. Within this strategy, independent neighborhoods are constructed for each winner to learn the structure of irregular PFs. To take advantage of the potential knowledge of winners, the switching competitive swarm optimizer is designed to provide more diverse search directions. The experimental results demonstrate the superiority of the proposed algorithm over several state-of-the-art evolutionary algorithms in tackling multi-objective optimization problems with irregular PFs.

Neural Net-Enhanced Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization.

The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner particles and then uses the winner particles to efficiently guide the search of the loser particles. This approach has very promising performance in solving large-scale multiobjective optimization problems (LMOPs). However, most studies of CSOs ignore the evolution of the winner particles, although their quality is very important for the final optimization performance. Aiming to fill this research gap, this article proposes a new neural net-enhanced CSO for solving LMOPs, called NN-CSO, which not only guides the loser particles via the original CSO strategy, but also applies our trained neural network (NN) model to evolve winner particles. First, the swarm particles are classified into winner and loser particles by the pairwise competition. Then, the loser particles and winner particles are, respectively, treated as the input and desired output to train the NN model, which tries to learn promising evolutionary dynamics by driving the loser particles toward the winners. Finally, when model training is complete, the winner particles are evolved by the well-trained NN model, while the loser particles are still guided by the winner particles to maintain the search pattern of CSOs. To evaluate the performance of our designed NN-CSO, several LMOPs with up to ten objectives and 1000 decision variables are adopted, and the experimental results show that our designed NN model can significantly improve the performance of CSOs and shows some advantages over several state-of-the-art large-scale multiobjective evolutionary algorithms as well as over model-based evolutionary algorithms.

Offline learning-based competitive swarm optimizer for non-linear fixed-charge transportation problems

The fixed-charge transportation (FCT) problem, an extension of the classical transportation problem, holds significance in practical logistics scenarios where fixed charges play a crucial role. Fixed charges categorize it as an NP-hard problem, posing challenges for conventional methods due to their inefficiency, high computational costs, and susceptibility to local optima. This paper introduces an enhanced evolutionary algorithm, offline learning-based competitive swarm optimization (OLCSO), tailored to address the FCT problems efficiently. OLCSO draws inspiration from offline learning, where particles benefit from the experiences of high performers. The algorithm incorporates (i) a ranking-based mechanism where losers learn from peer-to-peer winners based on their ranks, (ii) a two-phase cooperative evolutionary mechanism enhancing searchability and convergence of the algorithm. In the first phase, losers learn from personal best experiences and the shifted center of the population, boosting diversity. In the second phase, losers learn from winners and the center of the population, improving exploitation, (iii) a mutation strategy is embedded to update winner locations in each evolutionary procedure, and (iv) to ensure a feasible solution to FCT problems, the paper also introduces negative and fractional repair mechanisms. OLCSO is applied to solve two sets of FCT problems, linear and non-linear. Experimental results are compared with various heuristic and metaheuristic techniques, commonly used for these problems, using diverse metrics. A comparative analysis of OLCSO’s design elements is also conducted. The results demonstrate OLCSO’s superiority over existing algorithms.

Hybrid optimized deep quantum neural network in Internet of Things platform using routing algorithm for detecting smart maize leaf disease

SummaryThe productivity in the agricultural sector is minimized due to the disease in plants. In general, the ailments that affect plants are identified by the farmers and the losses are minimized, when the diseases are identified early. The early identification of leaf diseases is difficult in the traditional approaches. Hence, in this article, for detecting maize leaf disease, an adaptive competitive shuffled shepherd optimization‐driven deep quantum neural network (adaptive CSSO‐based deep QNN) is implemented. Here, the initial process is the simulation of the IoT nodes and the leaf data are collected. This data are transferred to base station (BS) via the best routes. The optimal routes are identified using the adaptive CCSO algorithm. The adaptive concept, shuffled shepherd optimization algorithm (SSOA) and competitive swarm optimizer (CSO) are merged for forming the adaptive‐CSSO algorithm. The leaf detection is done in the BS and initially, the data is preprocessed using region of interest (ROI). Then, the relevant features are extracted. Finally, the disease in the maize leaf is detected using Deep QNN and the training is done by adaptive CSSO. The devised approach has maximum accuracy of 96.04%, sensitivity of 97.41%, specificity of 94.35%, energy of 0.01 J, and minimum delay of 0.9596 s.

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

Competitive Swarm Optimizer: A decade survey

Since its inception in 2014, the Competitive Swarm Optimizer (CSO) has emerged as a significant advancement in the field of swarm intelligence, particularly in addressing large-scale optimization challenges. This paper offers a comprehensive review of a decade of developments in CSO research, synthesizing a vast array of research efforts to underscore the pivotal CSO variants, their foundational principles, and notable achievements. It encompasses a broad spectrum of applications, including single-objective, multi-objective, and engineering optimization problems, and introduces a meticulously constructed taxonomy to facilitate further investigation into CSO-related research. Additionally, this paper incorporates comparative analyses and in-depth investigations, thereby providing enhanced insight into both the historical evolution and future research directions of CSO.

Competitive Swarm Optimized SVD Clutter Filtering for Ultrafast Power Doppler Imaging.

Ultrafast power Doppler imaging (uPDI) can significantly increase the sensitivity of resolving small vascular paths in ultrasound. While clutter filtering is a fundamental and essential method to realize uPDI, it commonly uses singular value decomposition (SVD) to suppress clutter signals and noise. However, current SVD-based clutter filters using two cutoffs cannot ensure sufficient separation of tissue, blood, and noise in uPDI. This article proposes a new competitive swarm-optimized SVD clutter filter to improve the quality of uPDI. Specifically, without using two cutoffs, such a new filter introduces competitive swarm optimization (CSO) to search for the counterparts of blood signals in each singular value. We validate the CSO-SVD clutter filter on public in vivo datasets. The experimental results demonstrate that our method can achieve higher contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and blood-to-clutter ratio (BCR) than the state-of-the-art SVD-based clutter filters, showing a better balance between suppressing clutter signals and preserving blood signals. Particularly, our CSO-SVD clutter filter improves CNR by 0.99 ± 0.08 dB, SNR by 0.79 ± 0.08 dB, and BCR by 1.95 ± 0.03 dB when comparing a spatial-similarity-based SVD clutter filter in the in vivo dataset of rat brain bolus.

A time-varying competitive swarm optimizer for integrated flight recovery with multi-objective and priority considerations

Integrating aircraft and crew recovery is a growing topic in providing an efficient recovery system after a disruption. Most existing models only conduct from airline perspective and treat all disturbed flights homogeneously. However, in a realistic operation, the decision objectives are multiple with the interplay of stakeholders. Moreover, flights with first-aid items hold special priority. Thus, this paper presents a new Integrated Aircraft and Crew Recovery with Multi-objective and Priority, namely IACR_MP. To solve IACR_MP efficiently, an ad-hoc particle swarm optimization-based optimizer is designed. Time-varying tri-competition with different evolutionary mechanisms is developed to achieve a balance between exploitation and exploration capacities. Three repair schemes are built to prevent low feasibility caused by high constraints, and a self-exploration strategy is used to detect the potential zones of contemporary non-dominated solutions. Finally, six instances with different scales are used for algorithm performance analyses. Results illustrate that our algorithm has good solution quality and significantly outperforms its competitors in solving the IACR_MP. Moreover, our framework can provide a series of specific recovery schemes, providing decision support with more choices to airline managers.

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative and competitive multi-objective optimization method, which we refer to as LCCMO. We begin by presenting a multi-rule cooperative initialization approach to create a population that combines strong convergence and diversity. This diverse population forms the foundation for our optimization process. Next, we develop a multi-level cooperative global search strategy that explores effective genes within solutions from different angles and sub-problems. This approach enhances our search for optimal solutions. Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.

A modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization

Multi-objective evolutionary algorithms have demonstrated promising performance in solving multi/many-objective problems. However, their performance decreases sharply when dealing with multi-objective optimization problems with hundreds or thousands of decision variables, which prevent them from quickly converging to the Pareto front. To this end, this article proposes a modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization (LSMCSO-SS). In the initialization phase of the algorithm, we propose a space sampling method, which samples a set of individuals according to the upper and lower bounds of the decision space. Then, they are added to the initial population to guide the evolution of the algorithm. In the iteration process of the algorithm, we propose a modified competitive swarm optimizer (CSO). Different from the original CSO algorithm, we add a new velocity component to the losers to further improve the convergence speed. In the experiments, we compare our algorithm with seven state-of-the-art large-scale multi-objective evolutionary algorithms based on the inverted generational distance plus (IGD+) indicator upon nine large-scale multi-objective optimization benchmark problems with up to 5000 decision variables, and the numerous experimental results manifest that the proposed method performs the best on most test instances, which further demonstrates that it outperforms all the seven comparison algorithms.

Constrained multi-objective optimization with dual-swarm assisted competitive swarm optimizer

Many metaheuristic methods have been proposed and have shown promising performance in solving multi-objective optimization problems (MOPs). As a representative example, the competitive swarm optimizer (CSO) exhibits excellent performance in solving large-scale MOPs. However, CSO performs poorly when used straightforwardly for constrained MOPs with complex constraints or objective spaces. In this paper, we propose a dual-swarm-based CSO framework that co-evolves two swarms with different orientations: (i) CSO is improved using genetic algorithms, namely CSO-GA to form a convergence-directed swarm without considering constraints to accelerate the convergence. (ii) CSO is further improved by differential evolution, namely CSO-DE to form a search-directed swarm by considering constraints to enhance diversity and improving the search for the constrained Pareto front. Moreover, a new constraint relaxation method is proposed that combines the particle swarm feasible rate and current evolutionary generation to dynamically adjust the factor for decreasing or increasing constraint relaxation. Extensive experiments with 37 benchmark instances and nineteen real-world constrained MOPs demonstrate that our proposed algorithm yields significantly better or at least more competitive results than ten state-of-the-art methods.

A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection

Feature selection (FS) is an important data preprocessing technique that selects a small subset of relevant features to improve learning performance. However, it is also challenging due to its large search space. Recently, competitive swarm optimiser (CSO) has shown promising results in FS because of its potential global search ability. The main idea of CSO is to select two solutions randomly and then let the loser (worse fitness) learn from the winner (better fitness). Although such a search mechanism provides a high population diversity, it is at risk of generating unqualified solutions since the winner’s quality is not guaranteed. In this work, we propose a constrained evolutionary mechanism for CSO, which verifies the quality of all the particles and lets the infeasible (unqualified) solutions learn from the feasible (qualified) ones. We also propose a novel local search and a size-change operator that guide the population to search for smaller feature subsets with similar or better classification performance. A surrogate model, based on support vector machines, is proposed to assist both local search and the size-change operator to explore a massive number of potential feature subsets without requiring excessive computational resource. Results on 24 real-world datasets show that the proposed algorithm can select smaller feature subsets with higher classification performance than state-of-the-art evolutionary computation (EC) and non-EC benchmark algorithms.

User Interest Identification with Social Media Information using Natural Language and Meta-Heuristic Technique

As the number of Internet users and social networking apps has grown in recent years, interest-based recommendation systems have been more commonly used in practice. Given the vast quantity of data available from LinkedIn and Twitter, as well as the expanding number of users, it was critical to create a real-time framework for recommending and monitoring relevant tweets or posts based on the user's interests. Using association rules, the interests of social network users can be uncovered. A considerable number of association rules extracted from social networks were found to be mostly dependent on coverage requirements. After finding patterns of frequent and non-frequent patterns, a large number of non-frequent terms were eliminated in association rule mining. In order to reduce the complexity of the association rule mining process, the more relevant terms are selected by the Hybridized Competitive Swarm Optimizer and Gravitational Search Algorithm (CSO-GSA), which is utilized for association rule generation and classification using deep learning techniques. In this research, numerous relevant rules for human interest are identified. The numerical outcome of the proposed strategy is compared with existing state-of-the-art techniques. The proposed CSO with GSA outperforms the existing techniques.

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.