Cooperative Search Research Articles

Cooperative search model to detect a lost target based on its probabilistic distribution in a bounded zone

ABSTRACT This study uses two unit-speed searchers to solve the challenge of finding a randomly located target in a bounded region, such as searching for bodies found under the rubble resulting from the erosion of residential units due to horrific natural disasters. The search procedure begins at ( 0 , 0 ) . The region is subdivided into a limited even number of cells. The target’s position has a symmetric probability distribution. It is preferable to search in an optimal method in order to reduce the predicted time for discovering the target. An algorithm is used to generate the optimal search strategy. We discuss an example when the target has a symmetric truncated normal distribution. We provide an illustrated case to demonstrate this technique’s applicability.

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search algorithm with Nelder–Mead (OCSANM). This enhanced algorithm builds upon the cooperation search algorithm (CSA) by incorporating opposition-based learning (OBL) and the Nelder–Mead simplex search method. The primary application of this algorithm is the design of a fractional-order proportional–integral–derivative (FOPID) controller for a buck converter system. A comprehensive evaluation is conducted using statistical boxplot analysis, nonparametric statistical tests and convergence response comparisons to assess the algorithm’s performance and confirm its superiority over CSA. Furthermore, the FOPID-controlled buck converter system based on OCSANM is compared with two top-performing algorithms: one using a hybridized approach of Lévy flight distribution with simulated annealing (LFDSA) and the other employing the improved hunger games search (IHGS) algorithm. This comparison encompasses transient and frequency responses, performance indices and robustness analysis. The results reveal the notable advantages of the proposed OCSANM-based system, including 25.8% and 8.7% faster rise times, 26% and 8.8% faster settling times compared with the best-performing approaches, namely LFDSA and IHGS, respectively. In addition, the OCSANM-based system exhibits a 34.7% and 9.6% wider bandwidth than the existing approaches-based systems. Incorporating voltage and current responses of the buck converter’s switched circuit with the OCSANM-based FOPID controller further underscores the algorithm’s effectiveness. To provide a comprehensive assessment, the paper also compares the proposed approach’s time and frequency domain responses with those of 17 other state-of-the-art approaches attempting to control buck converter systems similarly. These findings affirm the effectiveness of the OCSANM in designing FOPID controllers for buck converter systems.

On Probabilistic Cooperative Search Model to Detect a Lost Target in N-Disjoint Areas

This paper presents a new probabilistic coordinated search technique for finding a randomly located target in n-disjoint known regions by using n-searchers. Each region contains one searcher. The searchers use advanced technology to communicate with each other. The purpose of this paper is to obtain the candidate utility function namely the expected value of the time for detecting the target. Additionally, to minimize this expected value given a restricted amount of time. We present a special case when the target has a multinomial distribution. This important for searching about a valuable target missing at sea or lost at wilderness area.

Planos de contingência e coordenação estadual do SUS na pandemia de covid-19

ABSTRACT The article analyzes the role of the states in coordinating the response of the Unified Health System (SUS) to the COVID-19 pandemic. This documentary research was based on the 26 state contingency plans published between February 2020 and October 2021. The state governments acted in most dimensions analyzed: health emergency coordination, federative articulation, regulation, technical support, communication, integrated planning, service provision, financing, and cooperation. The results suggest the leading role of state governments in SUS management, emphasizing the search for integration, alignment, and cooperation between the sectors involved, besides decision-making based on scientific guidance and evidence. In a context of polarization and federative dispute, the state experience valued federative autonomy, a cooperative stance, and the institutional capacities in the SUS.

СУЧАСНІ ТРАНСФОРМАЦІЇ В РЕГІОНІ САХЕЛЮ В КОНТЕКСТІ ФОРМУВАННЯ БАГАТОПОЛЯРНОГО СВІТУ

The problem of the article highlights the poorly researched problem of the formation of regional cen- ters of influence in the process of creating a multipolar world. It is emphasized that the traditional coordinate system of the world system is experiencing significant changes, new centers and centers of influence are being formed. It has been established that the African continent is of great interest to individual states, organizations, and corpo- rations, and its importance for ensuring global stability and predictability of development is becoming decisive. It is noted that in recent years the continent is going through a period of political and economic transformations with a rejection of mono-export specialization and a focus on diversified breakthrough strategies. This is accompanied by the final destruction of the remnants of economic colonialism. As an example, the multifaceted processes taking place in the Sahel zone, a zone of priority influence of France, were considered. It is shown how the latter gradually loses its cementing influence on African countries, which not only get rid of its political dictates, but also try to form alternative economic enclaves with their own currency. At the same time, the processes of intensifying the cooperation of the Sahel countries with the G20, BRICS, etc. were considered. A certain problem of breaking traditional contacts with the metropolis for the region has also been identified and further transformations in the near future have been predicted. It is shown that the recent political transformations in a number of leading countries of the Sahel have determined new vectors for the development of the region, which today is becoming one of the key priorities of the investment policy of India, China and others. Among the key countries of the region, Niger and Gabon are analyzed, which, becoming the path of final decolonization, largely determine the agenda and undermine the European monopoly. At the same time, this significantly affects the stability of both Africa and the EU. It was determined that the latter still retains the monetary union of African countries as the last source of influence, while simultaneously losing military and political. At the same time, African countries are faced with the task of the fastest search for optimal cooperation both on the continent and outside it.

Optimized Design of Second-Order Tellurium-Based Fiber Raman Amplifier Based on Cooperative Search Algorithm

Optimized Design of Second-Order Tellurium-Based Fiber Raman Amplifier Based on Cooperative Search Algorithm

FOPID Controller Design for a Buck Converter System Using a Novel Hybrid Cooperation Search Algorithm with Pattern Search for Parameter Tuning

This research introduces a novel metaheuristic algorithm, OCSAPS, representing an upgraded cooperation search algorithm (CSA) version. OCSAPS incorporates opposition-based learning (OBL) and pattern search (PS) algorithms. The proposed algorithm's application aims to develop a fractional order proportional-integral-derivative (FOPID) controller tailored for a buck converter system. The efficacy of the proposed algorithm is assessed by statistical boxplot and convergence response analyses. Furthermore, the performance of the OCSAPS-based FOPID-controlled buck converter system is benchmarked against CSA, Harris hawk optimization (HHO), and genetic algorithm (GA). This comparative analysis encompasses transient and frequency responses, performance indices, and robustness analysis. The outcomes of this comparison highlight the distinctive advantages of the proposed approach-based system. Moreover, the proposed approach's performance was compared with six other approaches used to control buck converter systems similarly regarding both time and frequency domain responses. Overall, the findings underscore the efficacy of the OCSAPS algorithm as a robust solution for designing FOPID controllers in buck converter systems.

Turkish-Israeli economic relations in the new normalisation environment

ABSTRACT Normalization indicates that states have opened a new page in their bilateral relations, and while it does not cover some problematic areas in relations and does not offer a final solution, it serves to create a reasonable basis for dialogue. The concept of normalisation includes the capacity to transform not only the political but also the economic dimension of Turkish-Israeli relations. This article contends that before the normalisation process in the bilateral relationship, the economic and especially the commercial dimension of the relations remained at a certain level and maintained its strong position even during tense diplomatic and political periods. In this context, the article draws attention to the fact that the normalization of Israel-Turkey relations is an ongoing process and aims to indicate that despite the existence of some disagreements, the search for cooperation on specific issues of common interest may continue.

Multi-robot cooperative search for radioactive sources based on particle swarm optimization particle filter

Effective management and monitoring of radioactive sources are crucial to ensuring nuclear safety, human health, and the ecological environment. A multi-robot collaborative radioactive source search algorithm based on particle swarm optimization particle filters is proposed. In this algorithm, each robot operates as a mobile observation platform using the latest observations to fuse into particle sampling. At the same time, the particle swarm optimization algorithm moves the particle set to a high-likelihood area to overcome particle degradation. In addition, each particle can learn from the search history of other particles to speed up the convergence of the algorithm. Lastly, the Dynamic Window Approach (DWA) for dynamic window obstacle avoidance is used to avoid obstacles in complex mountainous terrains to achieve efficient source search. Experimental results show that the search success rate of the proposed algorithm is as high as 95%, and its average search time is only 3.43 s.

Unmanned Aerial Vehicle cooperative search model for sea based on OODA

Unmanned Aerial Vehicle cooperative search model for sea based on OODA

Hybrid method based energy management of electric vehicles using battery-super capacitor energy storage

This paper presents a hybrid technique for managing the Energy Management of a hybrid Energy Storage System (HESS), like Battery, Supercapacitor (SC), and integrated charging in Electric Vehicle (EV). The proposed hybrid method combines the Namib Beetle Optimization (NBO) and Quantum Neural Networks (QNN) technique and is commonly known as the NBO-QNN approach. The proposed energy management technique reduces EV power use and maximizes battery life. QNN forecasts and combines power supply and charge levels to fulfill load needs. EV energy management uses NBO to regulate the output voltage, generate references, and regulate current continuously. Higher energy density battery and power density SC meet vehicle needs. An uncontrolled rectifier with a DC-to-DC buck converter balances charging and ensures energy transmission. The Proposed technique is implemented using the MATLAB platform, and its performance is compared to existing methods. HESS performance is evaluated by comparing it to existing systems. The research shows that the proposed strategy reduces the primary and secondary source stress, enhances performance of charging unit, and extends the life of battery. Furthermore, the NBO-QNN technique outperforms other existing methods, such as the Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA). The proposed method displays the best output in all existing Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA) methods. The result concludes that the NBO-QNN approach based on THD value is less than existing methods.

A niche-based evolutionary algorithm with dual cooperative archive for solving constrained multi-objective optimization problems

Constrained multi-objective optimization problems (CMOPs) are commonly encountered in engineering practice. The key to effectively solving these problems lies in achieving a timely balance between convergence, diversity, and feasibility during iterations. Furthermore, the appropriate utilization of infeasible solutions is crucial for identifying potential feasible regions. In order to accomplish this comprehensive objective, we propose a novel dual-stage constrained multi-objective evolutionary algorithm (CMOEA) called NACMOEA in this paper. It can be characterized by the following features: 1) Introducing a novel niche-based individual selection and infeasible solution utilization strategy to enhance convergence, diversity, and feasibility. 2) Presenting a cooperative search strategy assisted by dual archives to approximate the constrained Pareto front (CPF) from both feasible and infeasible perspectives, thereby improving the efficiency of obtaining the complete CPF. 3) Designing a new stage switch method based on non-dominant coverage rate to ensure proper completion of search stage switching. Extensive experiments demonstrate that NACMOEA exhibits competitive comprehensive performance when compared with other advanced CMOEAs.

Cooperative aerial search by an innovative optimized map-sharing algorithm

In this paper, the problem of cooperative search and tracking by multiple flying vehicles is studied. An algorithm is proposed based on optimal planning by minimizing a suitably defined cost function. In order to define the cost function, three maps are proposed: uncertainty map, detection probability map, and emitter map. In the uncertainty map, the geometrical status of the vehicle with respect to the region is considered and its suitability is measured. The detection probability map is defined such that the group of vehicles can detect new emitters that appear in their sensors’ coverage. Finally, the emitter map is a tool to keep tracking the detected emitters with acceptable error while searching the region. The assumption that the vehicles are equipped with only one commonly used radar range indicator sensor makes the proposed algorithms more applicable in the real world for a cost-effective system. The efficiency of the proposed method from the search, detection, and tracking points of view is verified through simulations.

A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

With growing attention focused on energy generation and environmental conservation, the operation of cascade reservoirs that considers power generation benefits and ecological flow requirements plays an increasingly important role in water resource and power systems. However, the traditional optimization methods may fail to solve complex cascade reservoirs operation models with strong spatial-temporal coupling physical constraints. To effectively address this problem, this study proposes an efficient multi-objective cooperation search algorithm (MOCSA) that utilizes the modified team communication, reflective learning operator and internal competition operators to enhance global exploration and local exploitation. MOCSA is tested on a group of multi-objective benchmark functions and real-world constrained engineering problems. The results demonstrate that MOCSA can provide better results for most benchmark test functions in comparison with the competitive algorithms. Besides, MOCSA exhibits excellent search ability to find feasible solutions of constrained engineering problems. The proposed method is then applied to resolve a real-world reservoir system under different operation scenarios. Simulations indicate that MOCSA provides diversified decision options for the operation of cascade reservoir system and find a more diverse set of non-dominated solutions within the feasible solution space. Overall, this research presents MOCSA as an effective multi-objective optimization tool for complex cascade reservoir operation problems.

A multi-agent reinforcement learning algorithm with the action preference selection strategy for massive target cooperative search mission planning

Target search is widely applied in military reconnaissance, geological exploration and personnel search and rescue. Most target search algorithms perform well in single target search but are inefficient or even ineffective in multi-target search. To solve the multi-target search problem in an uncertain environment, this paper constructs a multi-agent massive target cooperative search mission planning model and proposes an improved reinforcement learning algorithm using the action preference selection strategy. Based on the Reinforce algorithm, this algorithm solves the problem of invalid searches within the stochastic strategy by changing the preferred action selection method. The proposed method improves the efficiency of multiple agents in the search for targets without collision using a cooperative mechanism and reward rules based on the odor effect. Simulation experiments are conducted in three aspects to verify the effectiveness and robustness of the improved algorithm and compare it with other reinforcement learning algorithms in the field of multi-agent learning. The results demonstrate that the improved algorithm has obvious advantages in terms of mission success rate, target search rate and average search time, and the movement trajectory of multiple agents is more concise.

Multi-UAV Cooperative Searching and Tracking for Moving Targets Based on Multi-Agent Reinforcement Learning

In this paper, we propose a distributed multi-agent reinforcement learning (MARL) method to learn cooperative searching and tracking policies for multiple unmanned aerial vehicles (UAVs) with limited sensing range and communication ability. Firstly, we describe the system model for multi-UAV cooperative searching and tracking for moving targets and consider average observation rate and average exploration rate as the metrics. Moreover, we propose the information update and fusion mechanisms to enhance environment perception ability of the multi-UAV system. Then, the details of our method are demonstrated, including observation and action space representation, reward function design and training framework based on multi-agent proximal policy optimization (MAPPO). The simulation results have shown that our method has well convergence performance and outperforms other baseline algorithms in terms of average observation rate and average exploration rate.

Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs

This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.

Multi-AUV cooperative search method based on dynamic optimal coverage

This paper introduces a multi-AUV target search method founded on dynamic optimization coverage, aiming to plan paths that achieve complete coverage without backtracking and with reduced travel costs for participating AUVs in cooperative search missions. The study comprises three key components: 1) Environment Modeling:We present a mixed-type grid approach, capable of accurately modeling concave–convex polygons. 2) Area Allocation:A dynamic cooperative partition strategy based on K-means+ is employed to facilitate optimal area allocation for AUVs. 3) Path Planning: The paper introduces a search path planning algorithm called the Optimal Allocation Minimum Spanning Tree (OA-MST). OA-MST generates globally smooth and non-backtracking scanning paths within irregular task areas using an adaptive spanning tree. Moreover, the study includes multiple simulation experiments to validate the method’s efficacy. OA-MST is compared with the lawnmower strategy and four advanced Minimum Spanning Tree (MST) generation methods under identical area allocation conditions. The results demonstrate that the proposed collaborative search method significantly enhances path smoothness and reduces path costs across diverse scenarios. In addition, sea trials are conducted to verify the feasibility and practicality of implementing the proposed methods in engineering applications.

Research on Scheme Design and Decision of Multiple Unmanned Aerial Vehicle Cooperation Anti-Submarine Based on Knowledge-Driven Soft Actor-Critic

To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement learning algorithm that consists of two main components: UAV Group Search Knowledge Base (UGSKB) and path planning strategy. Firstly, based on the UGSKB, we establish a cooperation search framework that comprises three layers of information models: the data layer provides prior information and fundamental search rules to the system, the knowledge layer enriches search rules and database in continuous searching processes, and the decision layer utilizes above two layers of information models to enable autonomous decision-making by UAVs. Secondly, we propose a rule-based deductive inference return visit (RDIRV) strategy to enhance the knowledge base of search. The core concept of this strategy is to enable UAVs to learn from both successful and unsuccessful experiences, thereby enriching the search rules based on optimal decisions as exemplary cases. This approach can significantly enhance the learning performance of KD-SAC. The subsequent step involves designing an event-based UGSKB calling mechanism at the decision-making level, which calls a template based on the target and current motion. Finally, it uses a punishment function, and is then employed to achieve optimal decision-making for UAV actions and states. The feasibility and superiority of our proposed algorithm are demonstrated through experimental comparisons with alternative methods. The final results demonstrate that the proposed method achieves a success rate of 73.63% in multi-UAV flight path planning within complex environments, surpassing the other three algorithms by 17.27%, 29.88%, and 33.51%, respectively. In addition, the KD-SAC algorithm outperforms the other three algorithms in terms of synergy and average search reward.

Aquila-particle swarm based cooperative search optimizer with superpixel techniques for epithelial layer segmentation

The segmentation of epithelial layers from oral histopathology images plays a crucial role for early detection of oral cancer disease. As a result, more accurate segmentation of this layer is of the utmost importance for a Computer Aided Diagnosis (CAD) system. Therefore, this paper presents a superpixel image-based clustering technique using an improved Nature-Inspired Optimization Algorithm (NIOA), called the Cooperative Search (CS) algorithm. Here, superpixel image is utilized to construct a faster and noise-resistant oral histopathology image clustering method. The designed CS, on the other hand, is based on two popular NIOAs, the Aquila Optimizer (AO) and the Particle Swarm Optimizer (PSO), has better exploration-exploitation abilities. This CS has been used in the image clustering field to circumvent the problem of local optima trapping. On the other hand, a comparative investigation of three popular superpixel strategies with the proposed CS optimizer has been conducted to determine the optimal superpixel strategy for epithelial layer segmentation from clean as well as noisy oral histopathology images. Finally, the optimal superpixel strategy with CS has been tested with other state-of-the-art image segmentation techniques in the epithelial layer segmentation domain. The results obtained by the proposed segmentation technique are 98.82%, 96.70%, 97.60%, and 95.25%, in terms of average Accuracy, MCC, Dice, and Jaccard, respectively, which all are better than the cutting-edge segmentation methods. The proposed segmentation model is also evaluated over the leaf segmentation model and achieved better results visually and numerically. The optimization efficiency of the CS has also been measured over CEC2019 mathematical benchmark test functions and produced competitive results compared to other tested NIOAs.