Applying non-revisiting genetic algorithm to traveling salesman problem

In [1], we propose non-revisiting genetic algorithm (NrGA) and apply it to a set of bench mark real valued test functions. NrGA has the advantage that it is non-revisiting, i.e. a visited point will not be visited again. This provides an automatic mechanism for diversity maintenance which does not suffer from premature convergence. Another advantage is that it supports a parameter-less adaptive mutation mechanism. In this paper, we show how NrGA can be adapted to a real world combinatorial optimization problem - the famous traveling salesman problem (TSP). Comparison with genetic algorithm (GA) (with revisits and standard mutation) is made. It is shown that NrGA gives superior performance compared to GA. Moreover, it gives the same stable performance using different types of mutation operators. Moreover, turning off GA's mutation operator but only use the NrGA inherent parameter-less adaptive mutation gives the best performance. © 2008 IEEE.published_or_final_versio

A non-revisiting particle swarm optimization

In this article, a non-revisiting particle swarm optimization (NrPSO) is proposed. NrPSO is an integration of the non-revisiting scheme and a standard particle swarm optimization (PSO). It guarantees that all updated positions are not evaluated before. This property leads to two advantages: 1) it undisputedly reduces the computation cost on evaluating a time consuming and expensive objective function and 2) It helps prevent premature convergence. The non-revisiting scheme acts as a self-adaptive mutation. Particles genericly switch between local search and global search. In addition, since the adaptive mutation scheme of NrPSO involves no parameter, comparing with other variants of PSO which involve at least two performance sensitive parameters, the performance of NrPSO is more reliable. The simulation results show that NrPSO outperforms four variants of PSOs on optimizing both uni-modal and multi-modal functions with dimensions up to 40. We also illustrate that the overhead and archive size of NrPSO are insignificant. Thus NrPSO is practical for real world applications. In addition, it is shown that the performance of NrPSO is insensitive to the specific chosen values of parameters. © 2008 IEEE.published_or_final_versio

On the detection of nearly optimal solutions in the context of single-objective space mission design problems

When making decisions, having multiple options available for a possible realization of the same project can be advantageous. One way to increase the number of interesting choices is to consider, in addition to the optimal solution x*, also nearly optimal or approximate solutions; these alternative solutions differ from x* and can be in different regions – in the design space – but fulfil certain proximity to its function value f(x*). The scope of this article is the efficient computation and discretization of the set E of e–approximate solutions for scalar optimization problems. To accomplish this task, two strategies to archive and update the data of the search procedure will be suggested and investigated. To make emphasis on data storage efficiency, a way to manage significant and insignificant parameters is also presented. Further on, differential evolution will be used together with the new archivers for the computation of E. Finally, the behaviour of the archiver, as well as the efficiency of the resulting search procedure, will be demonstrated on some academic functions as well as on three models related to space mission design

A non-revisiting simulated annealing algorithm

In this article, a non-revisiting simulated annealing algorithm (NrSA) is proposed. NrSA is an integration of the non-revisiting scheme and standard simulated annealing (SA). It guarantees that every generated neighbor must not be visited before. This property leads to reduction on the computation cost on evaluating time consuming and expensive objective functions such as surface registration, optimized design and energy management of heating, ventilating and air conditioning systems. Meanwhile, the prevention on function re-evaluation also speeds up the convergence. Furthermore, due to the nature of the non-revisiting scheme, the returned non-revisited solutions from the scheme can be treated as self-adaptive solutions, such that no parametric neighbor picking scheme is involved in NrSA. Thus NrSA can be identified as a parameter-less SA. The simulation results show that NrSA is superior to adaptive SA (ASA) on both uni-modal and multi-modal functions with dimension up to 40. We also illustrate that the overhead and archive size of NrSA are insignificant, so it is practical for real world applications. © 2008 IEEE.published_or_final_versio

Ensuring the Alignment of Genetic/Epigenetic Designed Swarms.

. One of the major concerns of AI researchers and implementers is how to ensure that the systems stay aligned with the aspirations of the humans they interact with. This problem becomes even more complex for systems that develop their own operational rules and where multiple agents are involved. The paper addresses some of the implications of using genetic/epigenetic design techniques where the control structure is developed without direct human involvement. This presents particular difficulties in ensuring that the control protocols stay aligned with the desires of the instigators and do not cause unpredicted harm. It also explores how this problem is further complicated when the AI system has many agents. Modern control systems are often decentralized which provides a more robust solution than using a central controller. A specific example of this approach is Self-Organising Swarms where the agents act independently of the central control. From an alignment point of view, it generates particular problems. Not only must the individual agents act in the best human interest but the swarm as a collective must do it as well. This is difficult for a homogeneous swarm and no proposal for a heterogeneous one has yet been made. There have been and continue to be considerable research and discussions on how to create and what form a global AI ethics might take, but any progress has been slow. This is partly because even the 4 ISSN 1028-9763. Математичні машини і системи. 2022. № 1 Universal Declaration of Human Rights has difficulties. All the nations that have signed up to the UN Human Rights Declaration believe they are at least trying to implement it. The problem is in the interpretation where many signatories believe others are in breach. The same would apply to any universal AI ethics agreement. This paper proposes a solution where the AI systems’ basic ethics are individual but have to comply where they interface with either other AI entities or humans. Keywords: genetic/epigenetic algorithms, AI alignment, AI ethics

Rank-based ant system with originality reinforcement and pheromone smoothing

Ant Colony Optimization (ACO) encompasses a family of metaheuristics inspired by the foraging behaviour of ants. Since the introduction of the first ACO algorithm, called Ant System (AS), several ACO variants have been proposed in the literature. Owing to their superior performance over other alternatives, the most popular ACO algorithms are Rank-based Ant System (ASRank), Max-Min Ant System (MMAS) and Ant Colony System (ACS). While ASRank shows a fast convergence to high-quality solutions, its performance is improved by other more widely used ACO variants such as MMAS and ACS, which are currently considered the state-of-the-art ACO algorithms for static combinatorial optimization problems. With the purpose of diversifying the search process and avoiding early convergence to a local optimal, the proposed approach extends ASRank with an originality reinforcement strategy of the top-ranked solutions and a pheromone smoothing mechanism that is triggered before the algorithm reaches stagnation. The approach is tested on several symmetric and asymmetric Traveling Salesman Problem and Sequential Ordering Problem instances from TSPLIB benchmark. Our experimental results show that the proposed method achieves fast convergence to high-quality solutions and outperforms the current state-of-the-art ACO algorithms ASRank, MMAS and ACS, for most instances of the benchmark.This research work was funded by the European project PDE-GIR of the European Union’s Horizon 2020 research & innovation program (Marie Sklodowska-Curie action, grant agreement No 778035), and by the Spanish government project #PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER, EU “Una manera de hacer Europa”

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions

Linearization and analysis of level as well as thermal process using labview

The processes encountered in the real world are usually multiple input multiple output (MIMO) systems. Systems with more than one input and/or more than one output are called MIMO system. The MIMO system can either be interacting or non-interacting. If one output is affected by only one input, then it is called non-interacting system, otherwise it is called interacting system. The control of interacting system is more complex than the control of non-interacting system. The output of MIMO system can either be linear or non-linear. In process industries,the control of level, temperature,pressure and flow are important in many process applications.In this work, the interacting non -linear MIMO systems (i.e. level process and thermal process) are discussed. The process industries require liquids to be pumped as well as stored in tanks and then pumped to another tank. Most of the time the liquid will be processed by chemical or mixing treatment in the tanks, but the level and temperature of the liquid in tank to be controlled at some desired value and the flow between tanks must be regulated.The interactions existing between loops make the process more difficult to design PI/PID controllers for MIMO processes than that for single input single output (SISO) ones and have attracted attention of many researcher in recent years.In case of level process, the level of liquid in the tank is controlled according to the input flow into the tank. Two input two output (TITO) process and four input four outputs (FIFO) process are described in the thesis work. The aim of the process is to keep the liquid levels in the tanks at the desired values. The output of the level process is non-linear and it is converted into the linear form by using Taylor series method. By using Taylor series method in the non-linear equation, the converted linear equation for the MIMO process is obtained. The objective of the thermal process is to cool a hot process liquid.The dynamic behaviour of a thermal process is understood by analysing the features of the solutions of the mathematical models. The mathematical model of the thermal process is obtained from the energy balance equation. The nonlinear equation is linearized by using Taylor series. The responses of the higher-order thermal process (3 x 2 and 3 x 3) are obtained and analysed. Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) is used to communicate with hardware such as data acquisition, instrument control and industrial automation. Hence LabVIEW is used to simulate the MIMO system