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The increasing demand for food production with limited available water resources poses a threat to agricultural activities. Conventional optimization algorithms increase the processing stage and perform in the space allocated from user. Therefore, the proposed work was used to design better performance results. The conjunctive allocation of water resources maximizes the net benefit of farmers. In this study, the novel hybrid optimization model developed is the first of its kind and was designed to resolve the sharing of water resource conflict among different reaches based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of the water deficit in Sathanur reservoir command. The GA-based optimization model considered crop-related physical and economic parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reaches obtained from the BFO is considered as an input to the ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average productivity values, policy 3 (3.665 Rs/m3) has better values relating to policy 1 (3.662 Rs/m3) and policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising for enhancing farmer's net income and can be replicated in other irrigated regions to overcome chronic water problems. The productivity value of policy 3 was 6.54% greater than that of policy 2, whereas that of policy 1 was 6.45% greater. Overall, the comparison shows that the better performance analysis of various optimization was done successfully. HIGHLIGHTS Hybrid optimization model using three meta-heuristic algorithms (GA, BFO and ACO) was developed.; The developed hybrid model is used for optimizing the cropping area, conjunctive water allocation and net returns for different reaches.; The net returns from the developed hybrid model (GA-BFO-ACO) outperform GA- BFO model.; The developed hybrid model is the first of its kind to unravel the sharing of water resources for profit maximization which can be adopted globally to overcome chronic water problems.

The glass transition temperature (Tg) is an important decision parameter when synthesizing polymeric compounds or when selecting their applicability domain. In this work, the glass transition temperature of more than 100 homopolymers with saturated backbones was predicted using a neuro-evolutive technique combining Artificial Neural Networks with a modified Bacterial Foraging Optimization Algorithm. In most cases, the selected polymers have a vinyl-type backbone substituted with various groups. A few samples with an oxygen atom in a linear non-vinyl hydrocarbon main chain were also considered. Eight structural, thermophysical, and entanglement properties estimated by the quantitative structure–property relationship (QSPR) method, along with other molecular descriptors reflecting polymer composition, were considered as input data for Artificial Neural Networks. The Tg’s neural model has a 7.30% average absolute error for the training data and 12.89% for the testing one. From the sensitivity analysis, it was found that cohesive energy, from all independent parameters, has the highest influence on the modeled output.

This research acquired data from the Central Weather Bureau Observation Data Inquiry System (CODIS) for historical weather information, such as observation time, temperature, humidity, wind speed, global radiation, etc., and constructed a historical weather database by using Excel software. Least square support vector machine (LSSVM) was used to forecast wind speed and solar radiation, then, the power output of wind and solar was derived. Considering factors of the demand response and the load and electricity pricing, a maximized risk income model of the virtual power plant (VPP) is established based on conditional value-at-risk (CVAR). An enhanced bacterial foraging algorithm (EBFA) was proposed to solve the risk dispatch problem of a VPP in this paper. In an EBFA, the stochastic weight trade-off is embedded to improve the behavior pattern of individual bacteria to enhance their sorting efficiency and accuracy in a high-dimension solution space. Various moving patterns of EBFA were considered for improvement, which were demonstrated by using a VPP system on Penghu island, Taiwan. Many scenarios were created, including various seasons, power rebate pricings, and confidence levels, so the maximal risk and return of VPP could be simulated and analyzed. Simulation and tests show a positive result for a VPP to perform the power dispatch by maximizing risk income. This paper also provides a guideline for the VPP to handle the risk management.

The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.

Algorithms inspired by swarm intelligence have been used for many optimization problems and their effectiveness has been proven in many fields. We propose a new swarm intelligence algorithm for structural learning of Bayesian networks, BFO-B, based on bacterial foraging optimization. In the BFO-B algorithm, each bacterium corresponds to a candidate solution that represents a Bayesian network structure, and the algorithm operates under three principal mechanisms: chemotaxis, reproduction, and elimination and dispersal. The chemotaxis mechanism uses four operators to randomly and greedily optimize each solution in a bacterial population, then the reproduction mechanism simulates survival of the fittest to exploit superior solutions and speed convergence of the optimization. Finally, an elimination and dispersal mechanism controls the exploration processes and jumps out of a local optima with a certain probability. We tested the individual contributions of four algorithm operators and compared with two state of the art swarm intelligence based algorithms and seven other well-known algorithms on many benchmark networks. The experimental results verify that the proposed BFO-B algorithm is a viable alternative to learn the structures of Bayesian networks, and is also highly competitive compared to state of the art algorithms. Reproduction selects elite individuals and realizes information transmission.Chemotaxis and elimination-and-dispersal maintain a balance between exploitation and exploration.Four operators serve as candidate directions for each bacterium to select.