Abstract Owing to the recent technological innovations, unmanned aerial vehicles (UAVs) are progressively employed in various civil and military applications, including healthcare. This requires estimating an optimum route under various real‐world complexities, such as non‐uniform obstacles. However, most of the reported work considers only uniform obstacles as an object, which limit their practical applicability. Hence, Archimedes optimization algorithm (AOA) is examined to overcome this limitation. Further, it is observed that many a time, AOA over‐exploits the search space, resulting in higher computational time. Therefore, the present work fuses AOA with grey wolf optimizer (GWO) to improve the convergence capability. Also, reinforcement learning (RL) is employed to intelligently switch between the exploration and exploitation phases. The efficacy of the developed algorithm is statistically analysed and validated against various metaheuristics on several benchmark functions. The simulated results verified that the developed RLGA provides optimal or near‐optimal solutions more efficiently relative to other metaheuristics. Moreover, it also affirms the hypothesis that the proposed modifications significantly improve the convergence speed of AOA. Finally, the appropriateness of RLGA is tested and validated by rigorous experimentation on real‐world 3D‐route estimation problems for UAVs. The simulated results reveal that RLGA produces a flyable path with 51.46%, 62.06%, and 70.42% lesser cost than RLGWO, AOA, and GWO, respectively. This ensures the employability of RLGA for efficient medical assistance in minimum time‐, energy‐, and transportation‐cost with safe and smooth UAV auto‐navigation for developing drone doctors.

The Vehicle Routing Problem (VRP) is a crucial combinatorial optimization problem in transportation, logistics, and supply chain management. Quantum computing has gained interest in solving complex optimization issues. This review paper proposes a novel approach that combines quantum computing principles with the Feynman path integral formulation to address the VRP. The paper focuses on limited qubits and how quantum algorithms can circumvent this limitation. Conventional methods, such as Lagrangian and combinatorial optimization, require computational time to solve complex atomic-scale problems. The path integral formulation with the first Born approximation method offers a more elegant explanation of quantum computing. The algorithm investigates quantum system dynamics using the Schrodinger wave equation and computes probability amplitude distributions for optimal vehicle routing techniques. This work will take advantage of entanglement and interference effects for heuristic sampling of high-quality solutions, making the Quantum Path Integral Approach (QPIA) a promising study environment for investigating quantum circuits and algorithms in routing problems with constrained qubits.

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Differential Evolution (DE) algorithm is one of the popular evolutionary algorithms that is designed to find a global optimum on multi-dimensional continuous problems. In this paper, we propose a new variant of DE algorithm by combining a self-adaptive DE algorithm called dynNP-DE with Elite Opposi tion-Based Learning (EOBL) scheme. Since dynNP-DE algorithm uses a small number of population size in the later of the search process, the population diversity becomes low, and therefore premature convergence may occur. We have therefore extended an OBL scheme to dynNP-DE algorithm to overcome this shortcoming and improve the optimization performance. By combining EOBL scheme to dynNP-DE algorithm, the population diversity can be supplemented because not only the information of individuals but also their opposition information can be utilized. We measured the optimization performance of the proposed algorithm on CEC 2005 benchmark problems and breast cancer detection, which is a research field that has recently attracted a lot of attention. It was verified that the proposed algorithm could find better solutions than five state-of-the-art DE algorithms.

Wiener filter yields the minimum‐mean‐square error between the restored image and the original image. However, to obtain an optimal result, there must be accurate knowledge of the power spectra of the noise and the original image besides the degradation function. Otherwise, it will lead to an undesirable restored result. This study presents a so‐called blind‐Wiener filter that can restore the original image when we have no knowledge of the power spectra of both noise and original image. It uses the fact that averaging several consecutively measured images together will enhance signal‐to‐noise ratio (SNR). The number of images to be averaged to reduce noise to an acceptable level was concluded to be around ten. Ten independent random noises were added to a given corrupted image, resulting in ten images with different noises and then each of them was restored by Wiener filter to yield ten Wiener filtered images. Finally, the corrupted image was restored by taking an average over the ten Wiener filtered images. Experiments were conducted in a practical setting to demonstrate the effectiveness of the proposed method. The experimental results show that all the images in the test set were vastly improved and some images gave almost comparable performance to the traditional Wiener filter known as the best restoration method in terms of peak SNR.

Foraging behavior has inspired different algorithms to solve real-parameter optimization problems. One of the most popular algorithms within this class is the Artificial Bee Colony (ABC). In the present study the food source is initialized by comparing the food source with worst fitness and the evaluated mean of randomly generated food sources (population). Further the scout bee operator is modified to increase searching capabilities of the algorithm to sample solutions within the range of search defined by the current population. The proposed variant is called IFS-ABC and is tested on six unconstrained benchmark function. Further to test the efficiency of the proposed variant we implemented it on five constrained engineering optimization problems.

In this study we propose a data clustering method relying on a new genetic operator, which is based on the biological concept of segregation distortion genes. The proposed method repeatedly applies conditional recombination and mutation operators to a pair of randomly selected chromosomes from a population, whose initial members represent the target data to be clustered, thus increasing the population size. While doing so, so-called segregation distortion genes are recognized, which then separate the growing population into species. There, a species is characterized by a set of chromosomes that can yield new chromosomes by using standard genetic operators, while these operators cannot be applied between the set of chromosomes of different species (this way establishing above-mentioned conditional application of operators). This also indicates that the proportion of a particular allele on some locus within the whole population, in comparison to other alleles, can increase, thus giving raise for new segregation distortion genes, and new species, to appear. The assignment of the initial cluster data within the population to species gives the clustering result. The proposed method is demonstrated for the problem of clustering of bit strings, the processing is analyzed, and its feasibility is shown.

In this paper, we consider an optimal topology control problem that dynamically configures the path and capacity under the varying traffic load for constructing a multihop backbone network in the TDD-OFDMA system. In order to deal with the co-channel interference among the different relay stations, K orthogonal subzones of OFDMA data regions are spatially reused. We present a joint path and capacity design problem that maximizes the average throughput while properly trading off the outage performance. Due to the complexity of optimal solution approach to this problem, we resort to a genetic algorithm (GA) to solve the dynamic topology design problem and compare its solution to the results from the existing heuristic algorithms.

A customary paradigm of designing a competent optimization algorithm is to combine an effective global searcher with an efficient local searcher. This paper presents and analyzes an entropy-based substructural local search method (eSLS) for the Bayesian Optimization Algorithm (BOA). The local searcher (the mutation operator) explores the substructural neighborhood areas defined by the probabilistic model encoded in the Bayesian network. The improvement of each local search step can be estimated by considering the variation this mutation causes to the entropy measurement of the population. Experiments show that incorporating BOA with eSLS results in a substantial reduction in the number of costly fitness evaluations until convergence. Moreover, this paper provides original insights into how the randomness of populations can be exploited to enhance the performance of optimization processes.

In evolutionary algorithms, the efficiency enhancement techniques are capable of solving difficult large scale problems in a scalable manner. This paper rigorously analyzes the Bayesian optimization algorithm (BOA) incorporated with an innovative evaluation relaxation method based on the entropy measurement theory (en-BOA). In particular, the concept of entropy is used to develop the evaluation relaxation strategy (ERS) and to determine the rate of convergence. Entropy measurement-based ERS is employed to recognize which candidate solution should be evaluated by the actual function or be estimated by the surrogate model. Experiments prove that en-BOA significantly reduces the number of actual evaluations and the scalability of BOA is not negatively affected. Moreover, the entropy measurement-based evaluation relaxation technique does not require any larger population sizes.

This paper presents a framework of estimation of particle swarm distribution algorithms (EPSDAs). The aim lies in effectively combining particle swarm optimization (PSO) with estimation of distribution algorithms (EDAs) without losing on their unique features. To exhibit its practicability, an extended compact particle swarm optimization (EcPSO) is developed along the lines of the suggested framework. Empirical results have adduced grounds for its effectiveness.

Estimation of distribution algorithms (EDAs) are major tools in evolutionary optimization. They have the ability to uncover the hidden regularities of problems and then exploit them for effective search. Real-coded Bayesian optimization algorithm (rBOA) which brings the power of discrete BOA to bear upon the continuous domain has been regarded as a milestone in the field of numerical optimization. It has been empirically observed that the rBOA solves, with subquadratic scaleup behavior, numerical optimization problems of bounded difficulty. This underlines the scalability of rBOA (at least) in practice. However, there is no firm theoretical basis for this scalability. The aim of this paper is to carry out a theoretical analysis of the scalability of rBOA in the context of additively decomposable problems with real-valued variables. The scalability is measured by the growth of the number of fitness function evaluations (in order to reach the optimum) with the size of the problem. The total number of evaluations is computed by multiplying the population size for learning a correct probabilistic model (i.e., population complexity) and the number of generations before convergence, (i.e., convergence time complexity). Experimental results support the scalability model of rBOA. The rBOA shows a subquadratic (in problem size) scalability for uniformly scaled decomposable problems.

This paper provides empirical studies on MrBOA, which have been designed for strengthening diversity of nondominated solutions. The studies lead to modified sharing. A new selection scheme has been suggested for improving diversity performance. Empirical tests validate their effectiveness on uniformity and front-spread (i.e., diversity) of nondominated set. A diversity-preserving MrBOA (dp-MrBOA) has been designed by carefully combining all the promising components; i.e., modified sharing, dynamic crowding, and diversity-preserving selection. Experiments demonstrate that dp-MrBOA is able to significantly improve diversity performance (for the scaling problems), without weakening proximity of nondominated set.

Abstract — We consider the problem of minimizing the resources used for network coding while achieving the desired throughput in a multicast scenario. Since this problem is NPhard, we seek a method for quickly finding sufficiently good solutions. To this end, we take evolutionary approaches based on a Genetic Algorithm. In this paper, we extend the evolutionary algorithm that we previously proposed in three perspectives. First, whereas the previous algorithm can be applied to only acyclic networks, we devise a modified evaluation method that works also with networks with cycles. Second, we introduce a new set of GA components that in our experiments outperforms the one used in the previous algorithm. Third, we present a new framework of the evolutionary approach, where fitness evaluation and population management are done in a decentralized manner with a limited amount of coordination. The new framework enables a network coding protocol where the resources used for coding are optimized in the setup phase as the proposed evolutionary algorithm being loaded and run at each node of the network. We demonstrate the effectiveness of our algorithms by carrying out simulations on a number of different sets of network topologies. I.

This paper offers practical design-guidelines for developing efficient genetic algorithms (GAs) to successfully solve real-world problems. As an important design component, a practical population-sizing model is presented and verified.

This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient cGAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is to deal with issues connected with lack of memory by allowing a selection pressure that is high enough to offset the disruptive effect of uniform crossover. The pe-cGA finds a near optimal solution (i.e., a winner) that is maintained as long as other solutions generated from probability vectors are no better. The ne-cGA further improves the performance of the pe-cGA by avoiding strong elitism that may lead to premature convergence. It also maintains genetic diversity. This paper also proposes an analytic model for investigating convergence enhancement.

This paper presents a genetic algorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. This paper also develops a population-sizing equation that facilitates a solution with desired quality. It is based on the gambler ruin model; the equation has been further enhanced and generalized. The equation relates the size of the population, quality of solution, cardinality of the alphabet, and other parameters of the proposed algorithm. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types for almost all source-destination pairs. Furthermore, simulation studies emphasize the usefulness of the population-sizing equation. The equation scales to larger networks. It is felt that it can be used for determining an adequate population size in the SP routing problem.

This paper proposes a novel MAC protocol for wireless ATM networks, which is characterized by a contention-free mechanism of the reservation request and a deterministic nature of mobile-assisted (distributed) uplink scheduling under a framework of the dynamic reservation TDMA, as discussed in the current standardization activities of ETSI Project BRAN (broadband radio access network) and the wireless ATM working group in the ATM Forum. The design objective of the proposed MAC protocol is to guarantee the real-time constraint of the real-time VBR (rt-VBR) traffic class while maximizing the multiplexing gain among all ATM traffic classes, especially with a fixed length frame. The proposed deterministic scheduling scheme for the rt-VBR traffic class lends itself to implementing the minimal configuration of control data units for reservation request as desired under the limited wireless resources. Simulation experiments using statistically multiplexed MPEG-2 video streams are performed for a 25 Mbits/s wireless ATM access link scenario. It has been shown that the proposed framework guarantees the delay constraint of rt-VBR sessions along with its cell loss rate significantly reduced, while improving the average delay performance of the nrt-VBR in the range of 10%-30% without compromising the channel utilization as compared to the DSA++ system.