Cooperative Search Research Articles

Multi-objective optimization of the basic and single-stage Organic Rankine Cycles utilizing a low-grade heat source

This study deals with the multi-objective optimization of basic and single-stage Organic Rankine Cycles (ORC) utilizing a low-grade heat source. Twelve different isentropic and dry pure refrigerants are considered as the primary working fluid for two different ORC configurations. Specific Investment Cost (SIC) and second law efficiency of the thermodynamic cycle are considered for optimization objectives to be optimized seperately and concurrently. Sixteen and twenty two different decision variables are respectively taken into account for modeling of the basic and the single-stage ORC optimization problems. The optimization problem is solved by applying a swarm based metaheuristic optimizer called Artificial Cooperative Search (ACS) algorithm. A pareto curve comprised of non dominated optimal solutions is constructed for each refrigerant-cycle pair and the best answer among the set of non dominated solutions are chosen by means of TOPSIS decision making method. Thermodynamic performance of each refrigerant are evaluated with respect to numerical outcomes of the objective functions. Comparative analysis based on the efficiencies of problem objectives reveals that R236ea, R245fa and R600 are selected as the best performers of the basic ORC and R245ca, R245fa and R600 are selected as the best performers of the single-stage ORC. Finally, a sensitivity analysis is executed to observe the effect of the decision variables on the objectives. It is understood that the evaporator shell diameter, number of tube passes in the evaporator, evaporator pressure and mass flow rate of the refrigerant are the decision variables with the most influence on the design objectives.

PARENTAL ALIENATION WITHIN THE CONTEXT OF THE 1980 HAGUE CONVENTION ON THE CIVIL ASPECTS OF INTERNATIONAL CHILD ABDUCTION: THE BRAZILIAN PERSPECTIVE

The number of cases of relocation of children and adolescents to Brazil has increased significantly in the last years. One must consider that abducting or wrongfully retaining children from the places of their habitual residence prevents them from enjoying fundamental rights such as those to historical, social and cultural identities and even the right enjoy full family life with both sides of their families. This article shows that unilateral relocation of children to Brazil (as well their wrongful retention in Brazilian territory) in violation of the 1980 Hague Convention on the Civil Aspects of International Child Abduction, is actually one form of Parental Alienation practiced in cross border circumstances, pursuant to Federal Law nº 12.318, from August 26, 2010 (Brazilian Parental Alienation Act). On that sense, Brazilian administrative and judicial authorities must not only engender public policies and strategies to enhance acknowledgement about rules of the Convention among Brazilian communities living abroad, they must also encourage extrajudicial agreements between interested parties to increase the rates of voluntary return of abducted or retained children. In cases brought to courts, since Parental Alienation is a form of emotional abuse of the child, magistrates must count on the opinion of interdisciplinary advisors before considering opinion of the abducted children in the ruling of the return order. In sum, the search for international cooperation with other Contracting States of the 1980 Hague Convention and the respect of the best interest of the child must be in permanent harmony.

PARENTAL ALIENATION WITHIN THE CONTEXT OF THE 1980 HAGUE CONVENTION ON THE CIVIL ASPECTS OF INTERNATIONAL CHILD ABDUCTION: THE BRAZILIAN PERSPECTIVE

The number of cases of relocation of children and adolescents to Brazil has increased significantly in the last years. One must consider that abducting or wrongfully retaining children from the places of their habitual residence prevents them from enjoying fundamental rights such as those to historical, social and cultural identities and even the right enjoy full family life with both sides of their families. This article shows that unilateral relocation of children to Brazil (as well their wrongful retention in Brazilian territory) in violation of the 1980 Hague Convention on the Civil Aspects of International Child Abduction, is actually one form of Parental Alienation practiced in cross border circumstances, pursuant to Federal Law nº 12.318, from August 26, 2010 (Brazilian Parental Alienation Act). On that sense, Brazilian administrative and judicial authorities must not only engender public policies and strategies to enhance acknowledgement about rules of the Convention among Brazilian communities living abroad, they must also encourage extrajudicial agreements between interested parties to increase the rates of voluntary return of abducted or retained children. In cases brought to courts, since Parental Alienation is a form of emotional abuse of the child, magistrates must count on the opinion of interdisciplinary advisors before considering opinion of the abducted children in the ruling of the return order. In sum, the search for international cooperation with other Contracting States of the 1980 Hague Convention and the respect of the best interest of the child must be in permanent harmony.

PARENTAL ALIENATION WITHIN THE CONTEXT OF THE 1980 HAGUE CONVENTION ON THE CIVIL ASPECTS OF INTERNATIONAL CHILD ABDUCTION: THE BRAZILIAN PERSPECTIVE

The number of cases of relocation of children and adolescents to Brazil has increased significantly in the last years. One must consider that abducting or wrongfully retaining children from the places of their habitual residence prevents them from enjoying fundamental rights such as those to historical, social and cultural identities and even the right enjoy full family life with both sides of their families. This article shows that unilateral relocation of children to Brazil (as well their wrongful retention in Brazilian territory) in violation of the 1980 Hague Convention on the Civil Aspects of International Child Abduction, is actually one form of Parental Alienation practiced in cross border circumstances, pursuant to Federal Law nº 12.318, from August 26, 2010 (Brazilian Parental Alienation Act). On that sense, Brazilian administrative and judicial authorities must not only engender public policies and strategies to enhance acknowledgement about rules of the Convention among Brazilian communities living abroad, they must also encourage extrajudicial agreements between interested parties to increase the rates of voluntary return of abducted or retained children. In cases brought to courts, since Parental Alienation is a form of emotional abuse of the child, magistrates must count on the opinion of interdisciplinary advisors before considering opinion of the abducted children in the ruling of the return order. In sum, the search for international cooperation with other Contracting States of the 1980 Hague Convention and the respect of the best interest of the child must be in permanent harmony.

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles.

In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator

The recently proposed artificial cooperative search (ACS) algorithm is a population-based iterative evolutionary algorithm (EA) for solving real-valued numerical optimization problems. It uses a rotation-invariant line recombination-based mutation strategy and rule-based crossover operator. However, it performs poorly for problems that include closely-related variables because, in these cases, generating uncorrelated feasible trial solution vectors using stochastic crossover methods is extremely difficult, and its mutation and crossover operators are also less effective. This paper adds a new QR-decomposition-based rotation-invariant search strategy to the ACS algorithm to improve its ability to solve such problems. This new, advanced ACS algorithm, called A+, has only one control parameter, α, and experimental results have shown that its performance does not strongly depend on the initial value of α. This paper also examines A+’s performance for noisy point cloud filtering, which is a complex real-world problem. The results of numerical experiments demonstrate that A+’s performance when solving numerical and real-world problems with closely-related variables is better than those of the comparison algorithms.

A Cooperative Local Search Method for Solving the Traveling Tournament Problem

Constrained optimization is the process of optimizing a certain objective function subject to a set of constraints. The goal is not necessarily to find the global optimum. We try to explore the search space more efficiently in order to find a good approximate solution. The obtained solution should verify the hard constraints that are required to be satisfied. In this paper, we propose a cooperative search method that handles optimality and feasibility separately. We take the traveling tournament problem (TTP) as a case study to show the applicability of the proposed idea. TTP is the problem of scheduling a double round-robin tournament that satisfies a set of related constraints and minimizes the total distance traveled by the teams. The proposed method for TTP consists of two main steps. In the first step, we ignore the optimization criterion. We reduce the search only to feasible solutions satisfying the problem's constraints. For this purpose, we use constraints programming model to ensure the feasibility of solutions. In the second step, we propose a stochastic local search method to handle the optimization criterion and find a good approximate solution that verifies the hard constraints. The overall method is evaluated on benchmarks and compared with other well-known techniques for TTP. The computational results are promising and show the effectiveness of the proposed idea for TTP.

An efficient multi-core implementation of the Jaya optimisation algorithm

ABSTRACTIn this work, we propose a hybrid parallel Jaya optimisation algorithm for a multi-core environment with the aim of solving large-scale global optimisation problems. The proposed algorithm is called HHCPJaya, and combines the hyper-population approach with the hierarchical cooperation search mechanism. The HHCPJaya algorithm divides the population into many small subpopulations, each of which focuses on a distinct block of the original population dimensions. In the hyper-population approach, we increase the small subpopulations by assigning more than one subpopulation to each core, and each subpopulation evolves independently to enhance the explorative and exploitative nature of the population. We combine this hyper-population approach with the two-level hierarchical cooperative search scheme to find global solutions from all subpopulations. Furthermore, we incorporate an additional updating phase on the respective subpopulations based on global solutions, with the aim of further improving the convergence rate and the quality of solutions. Several experiments applying the proposed parallel algorithm in different settings prove that it demonstrates sufficient promise in terms of the quality of solutions and the convergence rate. Furthermore, a relatively small computational effort is required to solve complex and large-scale optimisation problems.

Heterogeneous multi-agent optimization framework with application to synthesizing optimal nuclear waste blends

Multi-agent optimization method is a nature-inspired framework that supports the cooperative search of an optimal solution of an optimization problem by a group of algorithmic agents connected through an environment with certain predefined information sharing protocol. In this work, we propose a novel heterogeneous multi-agent optimization (HTMAO) framework. The proposed framework is validated using a set of benchmark problems a real-world synthesizing radioactive waste blending problem. The optimal radioactive waste blending problem is formulated as a mixed integer nonlinear programming. The total frit used for vitrification process is minimized subject to thermodynamic properties and process model constraints. The model simultaneously determines the optimal decisions that include the combination of the waste tanks that form each waste blend and the amount of frit needed for the vitrification of each waste blend. In developing the HTMAO framework, efficient ant colony optimization algorithms; efficient simulated annealing; efficient genetic algorithm; and sequential quadratic programming solver are considered as algorithmic agents. We illustrate this approach through a real-world case study of the optimal radioactive waste blending of Hanford site in Southern Washington where nuclear waste is stored.

Optimal automatic generation control of two-area power systems with energy storage units under deregulated environment

An attempt is made in this paper to present the application, design, and performance analysis of a novel optimal controller (OC) for automatic generation control (AGC) of interconnected two-area electrical power systems in a deregulated power environment with energy storage units. The OC is designed via full state vector feedback strategy to carry out the study. Swift acting energy storage units such as redox flow batteries (RFBs) are integrated into the power system models, and their efficacy in boosting AGC performance is executed and compared. Initially, the efficacy of OC is investigated in a restructured two-area single-source thermal system, and then, the study is extended to a proposed more realistic restructured two-area multi-source thermal-hydro-diesel-gas power system. It is observed that OC is able to satisfy the AGC requirement under different power contracts in the open electricity market and shows better performance in comparison to a recently published artificial cooperative search algorithm optimized proportional integral controller. MATLAB simulation results demonstrate the improvements in the dynamic performance of the system in the presence of RFB. Sensitivity analysis reveals the robustness of OC under ample variations in the system parameters, initial loading and size and positions of uncontracted load power demands.

Comparison of Gradient Estimation in Cooperative Multi-Agent Source Seeking

Agent-based source seeking problem is addressed in this paper. This problem is relevant in, e.g., in hazardous gas leak in a chemical disaster. In the cooperative search, agents develop a formation to effectively search the source by communicating one to another via a communication topology. The source, or the search target, is represented by a scalar field y which might describe a temperature level, hazardous concentration of substances or vapor. Every agent has the information on its own position and the value of y at any instance. The agents are identical modeled as single and double integrator. Consensus filter is used to control the agent formation and comparison three types of gradient estimation are employed to search the source. Experiments show that the proposed schemes give good performance to solve cooperative search for source seeking problem

Hybrid Artificial Cooperative Search - Crow Search Algorithm for Optimization of a Counter Flow Wet Cooling Tower

In this paper, an improved version of Artificial Cooperative Search (ACS) algorithm is applied on a counter flow wet-cooling tower design problem. The Merkel’s method is used to determine the characteristic dimensions of cooling tower, along with empirical correlations for the loss and overall mass transfer coefficients in the packing region of the tower. Basic perturbation schemes of the Crow Search Algorithm, a recent developed metaheuristic algorithm inspired by the food searching behaviors intelligent crows, are incorporated into ACS to enhance the convergence speed and increase the solution diversity of the algorithm. In order to assess the solution performance of the proposed method, fourteen widely known optimization test function have been solved and corresponding convergence histories has been depicted. .Then the improved ACS algorithm (IACS) is applied on six different examples of counter flow wet-cooling tower optimization problem. The results obtained by applying the proposed algorithm are compared with the results of some other algorithms in the literature. Optimization results show that IACS is an effective algorithm with rapid convergence performance for the optimization of counter flow wet-cooling towers.

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

Abstract Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

Cooperative two-engine multi-objective bee foraging algorithm with reinforcement learning

This paper proposes a novel multi-objective bee foraging algorithm (MOBFA) based on two-engine co-evolution mechanism for solving multi-objective optimization problems. The proposed MOBFA aims to handle the convergence and diversity separately via evolving two cooperative search engines with different evolution rules. Specifically, in the colony-level interaction, the primary concept is to first assign two different performance evaluation principles (i.e., Pareto-based measure and indicator-based measure) to the two engines for evolving each archive respectively, and then use the comprehensive learning mechanism over the two archives to boost the population diversity. In the individual-level foraging, the neighbor-discount-information (NDI) learning based on reinforcement learning (RL) is integrated into the single-objective searching to adjust the flight trajectories of foraging bee. By testing on a suit of benchmark functions, the proposed MOBFA is verified experimentally to be superior or at least comparable to its competitors in terms of two commonly used metrics IGD and SPREAD.

Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.

A potential game approach to multiple UAV cooperative search and surveillance

In this paper, we developed a game theoretic formulation for multiple unmanned aerial vehicle (UAV) cooperative search and surveillance. The cooperative search problem is decomposed into three sequential tasks: coordinated motion, sensor observation, and cooperative information fusion. Firstly, the coordinated motion is designed as a multi-player potential game with constrained action sets. Then the binary log-linear learning is adopted to perform motion control, which guarantees optimal coverage. Then a consensus based fusion algorithm is introduced to construct the probability map to guide the following coordinated motion. Finally, simulations are performed to validate the effectiveness of our proposed approach. The modular framework enables the separate design of utility functions and learning algorithms, which offers a flexible way to accommodate different global objectives and underlying physical constraints.

“Nearest Zero-Point” Algorithm for Cooperative Robotic Search Missions

Four path planning and data exchange algorithms for cooperative search and coverage robotic missions are proposed and modified. The introduced methods are simulated using C++ programming environment and the results are discussed in detail for environments with static obstacles. It has been shown that using the nearest zero-point algorithm can greatly optimise the mission duration and also overlapping of the search trajectories. Finally, the results are compared with several existing algorithms.

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Abstract In this paper, an artificial cooperative search (ACS) algorithm tuned adaptive network-based fuzzy inference system (ANFIS) controller for optimal gain tuning of load frequency control (LFC) operation in deregulated scenario has been offered. The conventional controllers for load frequency control operation are having fixed gain values intended for nominal operating conditions of the power system and they do not afford effective and efficient performance over a large range of operating scenarios in the deregulated environment. To progress the system performance to its near optimum for all probable operating circumstances of the power system, the controller gains have to be computed for the equivalent operating conditions by using the restructured parameters. For this intention, a controller based on an adaptive network-based fuzzy inference system seems to be the most excellent and valuable preference. The ANFIS is trained by off-line data obtained using a new optimization technique, artificial cooperative search optimization algorithm and the corresponding gains are updated in real-time as per the changing operating conditions. ACS is a swarm intelligence algorithm developed for solving numerical optimization problems. The swarm intelligence philosophy behind ACS algorithm is based on the migration of two artificial superorganisms as they biologically interact to achieve the global minimum value pertaining to the problem. To exhibit the competence and robustness of the projected ACS algorithm tuned ANFIS controller, the controller has been implemented on a two-area two-unit interconnected deregulated power system having one reheat unit and one non-reheat unit in each area. The simulation results exhibit the ability of the designed ACS algorithm tuned ANFIS controller for online LFC operation in deregulated environment.

“Nearest Zero-point” Algorithm for Cooperative Robotic Search Missions

<p><span lang="EN-AU">Four path planning and data exchange algorithms for cooperative search and coverage robotic missions are proposed and modified. The introduced methods are simulated using C++ programming environment and the results are discussed in detail for environments with static obstacles. It has been shown that using the <strong>“nearest zero-point”</strong> algorithm can greatly optimize the mission duration and also overlapping of the search trajectories. Finally, the results are compared with several existing algorithms.</span></p>

Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times

This paper proposes a total of nine algorithms to minimize the makespan for the hybrid flowshop scheduling problem with sequence-dependent setup times. The first six algorithms are trajectory-based metaheuristics, including three variants of iterated local search and three variants of iterated greedy. The remaining three algorithms are population-based metaheuristics, namely, the improved fruit fly optimization, the improved migrating birds optimization, and the discrete artificial bee colony optimization. We present some advanced and effective technologies, including three mixed neighborhood structures, an enhanced perturbation method, and an enhanced destruction and construction procedure for the trajectory-based metaheuristics. We propose a path-relinking-based cooperative search, a diversity control scheme, and a diversified initialization approach for the improved fruit fly optimization. We calibrate the parameters and operators for the proposed algorithms by means of a design of experiments approach. To evaluate the proposed algorithms, we present several adaptations of other recent well-known meta-heuristics for the problem and conduct a comprehensive set of computational and statistical experiments to demonstrate the effectiveness of the presented algorithms. Among them, the discrete artificial bee colony optimization is the best-performing algorithm and it is able to improve 126 out of the 240 best known solutions for the benchmarks in the literature.