In this chapter, evolutionary computation techniques, algorithms and research are presented for the optimization and allocation problems. Several aspects of continuous optimization, systems security and supply networks (SN) are illustrated. The real-life optimization and security problems in systems, automation, SN and law enforcement are NP-hard optimization problems, thus evolutionary algorithms (EA) that employ metaheuristic methods are useful for solving them. EA gain significant interest in recent years, and this chapter summarizes some of the advances in that field and then summarizes their applications for real-life problems. The rest of this chapter is organized as follows. First, the introduction of the developments of nature-inspired EAs and metaheuristics is described. Then the working principles of genetic algorithms (GA), swarm intelligence, and other nature-inspired optimization algorithms are given. Next, the overview of the various applications that were solved and optimized by EAs is presented. The reader of this chapter will be familiar with the following topics: The state-of-the-art AI algorithms and techniques and their working principle. The way to harness AI for optimization and finding optimal solutions. Controlling and optimizing a collaborative system in real-time while addressing several tasks in a complex environment

To deal with the underlying heterogeneous law enforcement problem (LEPH), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priory. Addressing this challenge and inspired by real police logs, this research aims to solve the LEPH problem by using and comparing three methods: Fisher market-based FMC_TAH+, swarm intelligence HDBA, and Simulated Annealing SA algorithms. The three methods were compared in this study for the performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TAH+ both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TAH+ leads to 34% and 32% higher team utility in the highest shift workload.

The common method for testing metaheuristic optimisation algorithms is to benchmark against problem test suites. However, existing benchmark problems limit the ability to analyse algorithm performance due to their inherent complexity. This paper proposes a novel benchmark, BTB, whose member functions have known geometric properties and critical point topologies. A given function in the benchmark is a realisation of a specified barrier tree in which funnel and basin geometries, and values and locations of all critical points are predetermined. We investigate the behaviour of two metaheuristics, PSO and DE, on the simplest manifestations of the framework, ONECONE and TWOCONES, and relate algorithm performance to a downhill walker reference algorithm. We study success rate, defined as the probability of optimal basin attainment, and inter-basin mobility. We find that local PSO is the slowest optimiser on the unimodal ONECONE but surpasses global PSO in all TWOCONES problems instances below 70 dimensions. DE is the best optimiser when basin difference depths are large but performance degrades as the differences become smaller. LPSO is the superior algorithm in the more difficult case where basins have similar depth. DE consistently finds the optimum basin when the basins have equal size and a large depth difference in all dimensions below 100D; the performance of LPSO falls away abruptly beyond 70D.

Police officers conduct routine patrols and perform tasks in response to reported incidents. The importance of each task varies from low (e.g. noise complaint) to high (e.g. murder). The workload associated with each task, indicating the amount of work to be completed for the incident to be processed, may vary as well. Multiple officers with heterogeneous skills may work together on important tasks to share the workload and improve response time. To deal with the underlying law enforcement problem (LEPH), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priori. Addressing this challenge and inspired by real police logs, this research aims to solve the LEPH problem by using and comparing three methods: Fisher market-based FMC_TA(H+), swarm intelligence HDBA, and Simulated Annealing SA algorithms. FMC_TA(H+) is implemented, using agents as buyers and tasks as goods, to compute fair allocations (i.e. envy-free), and efficient (i.e. Pareto-optimal) in a polynomial or pseudo-polynomial time. FMC_TA(H+) allocations are heuristically scheduled, considering inter-agent constraints on shared tasks. HDBA, a probabilistic swarm intelligence algorithm inspired by the emergent behavior of social bees, was previously implemented to allocate agents to tasks based on agent performance, task priorities, and distances between agents and task-execution locations. SA is a meta-heuristic for approximating the global optimums in large optimization problems. The three methods were compared in this study for five different performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TA(H+) both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TA(H+) leads to 34% and 32% higher team utility in the highest shift workload.

This book provides theory and applications of novel methods developed for multi sensor systems and describes a dual-layer task allocation system that uses a new swarm intelligence algorithm for heterogeneous sensors and protocols to overcome problems of overloading, deception, and tampering

This chapter describes three examples of multi-agent systemsMulti-agent systems: a multi-sensory securitySecurity system for supply networksSupply networks, a multi-agent approach to solve the travelling salesman problem and multiple police officers allocated to crime incidents in law enforcement problem. These examples are evaluated by taskTask allocationAllocation algorithms to demonstrate the methods described in Chap. 5 . The comparison of the performances of nine state-of-the-art algorithms for these case studies is conducted in terms of tasks completion times and the number of unallocated tasks. Scalability and the influence of bias parameters of HDBA were analyzed for different numbers of sensors and tasks.

This chapter describes an example of a multi-sensory securitySecurity system for supply networks with disturbancesDisturbances. This example is evaluated by the use of a dual-layer system to illustrate the methods described in Chap. 7 .

This chapter presents an analysis of algorithms and protocols for sensorSensor allocation in a dual-layer system. The dual-layer system framework is described, taskTask administration protocolsTask administration protocols are presented, and the system with TAPs and HDBA that is able to handle the scenarios of deception, overloading, and tampering is defined in the current chapter. Figure 7.1 illustrates the dual-layer system architecture.

This chapter presents the analysis methods and the performance measures applied in this book. The described methods are evaluated by the use of extended examples in Chaps. 6 and 8 . The evaluations include task attendance analyses that are conducted for both a single-layerSingle-layer and a dual-layer system to examine the effect and the performance of the described frameworkFramework and algorithms.

This chapter defines the multi–sensorSensor task allocation in a single-layerSingle-layer system. The allocationAllocation problem is described and algorithms for multi-agent and multi-sensor task allocation are presented. Figure 5.1 illustrates the Layer 1 architecture of the system.

This chapter describes and analyzes analytically the performance of HDBA in ‘Layer 1’ and the performances of TAPs in ‘Layer 2’ of the taskTask allocationAllocation system (Fig. 10.1). An analysis of a simplified system consisting of two sensorsSensor and two tasks is presented. This analysis was conducted to reveal the basic operating mechanism of HDBA and to analyze the benefit of the collaborationCollaboration of agents in the allocation process in ‘Layer 1’ and the performance of a monitoring layerMonitoring layer that is dedicated to handling special cases by applying TAPs in ‘Layer 2’.

Multi-agent allocationAllocation has become a popular area of research and has advanced significantly in recent years in many applications such as multi-robot taskTask allocationMulti-robot task allocation, path planning, control of unmanned aerial vehicles, communication networks, conflict and error prevention, and formation of mobile robotsRobot [1, 12, 51, 55, 61]. Multi-agent task allocation problems consist of a set of agentsAgent and a set of tasks that the agents must execute [31, 57]. According to Gerkey and Matarić [20] and Robin and Lacroix [44], tasks can be divisible, i.e., each task can be performed by an individual or by a group of agents, and may also require collaborationCollaboration between agents. The problems of task allocation considered in the literature are mainly multi-agent problems, hence the question of centralized and decentralizedDecentralized systems arises [44]. There are diverse algorithms that are intended to solve task allocation [38, 42, 48, 53, 56]. In general, the multi-agent task allocation approaches can be divided into three categories: centralized, decentralizedDecentralized, and hybrid approaches [60]. The objective function of these approaches is to maximize the overall utilityUtility or to minimize the cost of performing the tasksTask by the agents under a variety of constraints.

There are plentiful applications of multi-sensor taskTask allocationAllocation in practice. The previous chapters of this book specified, formulated, described and illustrated the impacts of optimal multi-sensor task allocation on the quality and performance of supply networksSupply networks and law enforcement systems. Emerging applications and technologies of the future will include sensorsSensor with different abilities that need to be dynamically allocated to various and multiple tasks.

This chapter describes and analyzes the ‘AvailabilityAvailability module’ of the taskTask allocationAllocation system (Fig. 9.1). This chapter presents the reliability design and availability analysis of the system to further optimize system performance in case of sensorSensor failure and to ensure fault tolerantFault tolerant allocation system performance. Availability optimizationOptimization of the sensors’Sensor operation and comparison of the dual-layer system performance with optimized sensors’ availability to a regular non-optimized dual-layer system was conducted for two system types using Monte Carlo simulationsSimulation.

Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors' performance, tasks' priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems.

This research proposes a multi-sensor task allocation framework for security of supply networks aimed to maximise the number of correctly detected and reported security events (defined as tasks). The framework includes a double layer system consisting of a process layer and a monitoring layer. The process layer allocates sensors to tasks using an ant colony algorithm. The monitoring layer applies four task administration protocols (TAPs) specially developed and implemented to deal with high time-consuming tasks, conflicts in task priorities and sensor failure, defined in this research as overloading, deception and tampering of sensors, respectively. A system objective function for sensor to task allocation was developed to allow computation of the expected value of system performance given the sensor and the task parameters. Sensory limitations evaluated including reliability, distance coverage and the limited number of sensors are addressed in the decision-making process. The framework enables detection of tasks as soon as they occur in every location along the supply network, based on the sensor network distribution. The dual layer system analyses reveal that TAPs increase the systems performance in the scenarios of deception, tampering and overloading by more than 64% with respect to the number of unallocated tasks in comparison to a single layer system. Overall availability was analysed using Monte Carlo simulation and the fault tolerant system yielded significantly increased number of treated tasks (by 11%, p = 0.02).

Protocols for real-time collaboration of multiple agents for supply chain security tasks utilizing RFID information with real-time sensor tracking and monitoring are presented. The proposed system provides a structured framework and a set of interaction services by controlling a network of agents; each of the agents has a set of skills and resources that they contribute to the system. Optimal control through task administration protocols of agents' collaboration is developed. Simulation analysis indicated improved performance of 11% by optimal task assignment and agent's collaboration, with improved time management flexibility.

This paper presents a set of algorithms that are developed for real-time dynamic switching between collaboration levels in a human-robot target recognition system. The algorithms were developed for a closed-loop controller to maximize system performance, despite deviations in the parameter values. These developments enable smooth real-time adaptation of the combined human-robot system to many possible changes of the environment, human operator, and robot performance. System performance was analyzed in simulations for a variety of target probability distributions. Two hundred independent simulations for each target probability distribution were conducted to calculate algorithm performance for a variety of conditions. Values for human operations were taken from a target recognition experiment dealing with detecting melons for a robotic melon harvester. The numerical analysis results indicated that the developed dynamic switching algorithms resulted in improved system performance that, in some cases, was increased by more than 90%.

This paper presents a set of algorithms developed for real-time dynamic switching between collaboration levels in a human-robot target recognition system. The algorithms were developed for a closed-loop controller to maximize system performance despite deviations in the parameter values and were evaluated by conducting a thorough simulation analysis. These developments enable smooth real-time adaptation of the combined human-robot system to many possible changes of the environment, human operator and robot performance. System performance was analyzed in simulations for a variety of target probabilities distributions. Improvements that can be achieved by each algorithm were calculated as a mean value for 200 independent simulations for each target probability distribution. The numerical analysis results indicated that the developed algorithms for dynamic switching achieved improved system performance.