Measuring optimiser performance on a conical barrier tree benchmark

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

On the Optimization of Systems Using AI Metaheuristics and Evolutionary Algorithms

In this chapter, evolutionary computation techniques, algo- rithms 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 sig- nificant 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 meta- heuristics is described. Then the working principles of genetic algorithms (GA), swarm intelligence, and other nature-inspired optimization algo- rithms 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 environmen

A modified distributed bees algorithm for multi-sensor task allocation

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 was partially supported by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological and Cognitive Robotics Initiative, the Marcus Endowment Fund, and the Rabbi W. Gunther Plaut Chair in Manufacturing, by the PRISM Center at Purdue University, by the Beatriu de Pinós grant No. 2013 BP-B 00239 of the Catalan Government and by the EU-funded Marie Curie Actions COFUND programme.Peer Reviewe

A modified distributed bees algorithm for multi-sensor task allocation

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.Peer Reviewe

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation

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.Peer ReviewedPostprint (published version