Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested

Startup’s critical failure factors dynamic modeling using FCM

The emergence of startups and their influence on a country's economic growth has become a significant concern for governments. The failure of these ventures leads to substantial depletion of financial resources and workforce, resulting in detrimental effects on a country's economic climate. At various stages of a startup's lifecycle, numerous factors can affect the growth of a startup and lead to failure. Numerous scholars and authors have primarily directed their attention toward studying the successes of these ventures. Previous research review of critical failure factors (CFFs) reveals a dearth of research that comprehensively investigates the introduction of all failure factors and their interdependent influences. This study investigates and categorizes the failure factors across various stages of a startup's life cycle to provide a deeper insight into how they might interact and reinforce one another. Employing expert perspectives, the authors construct fuzzy cognitive maps (FCMs) to visualize the CFFs within entrepreneurial ventures and examine these factors' influence across the four growth stages of a venture. Our primary aim is to construct a model that captures the complexities and uncertainties surrounding startup failure, unveiling the concealed interconnections among CFFs. The FCMs model empowers entrepreneurs to anticipate potential failures under diverse scenarios based on the dynamic behavior of these factors. The proposed model equips entrepreneurs and decision-makers with a comprehensive understanding of the collective influence exerted by various factors on the failure of entrepreneurial ventures

Making sense of changing coastal systems: overcoming barriers to climate change adaptation using fuzzy cognitive mapping

This thesis describes the role and value of Fuzzy Cognitive Mapping (FCM) in undertaking coastal climate change adaptation at the local scale, comparing FCM against existing, scenario-based adaptation methods in overcoming known barriers to adaptation. It describes the attributes and limitations of FCM as a modelling tool, exploring what must be accounted for in considering the use of FCM in mixed stakeholder settings where individual and group knowledge must be integrated to form a view of the system under study, discussing in some detail the facilitation strengths and weaknesses inherent to the method. These issues are then described via reference to case-studies in Ireland and Scotland, drawing inferences regarding the ease with which an FCM-based approach to adaptation might be substituted for orthodox, scenario-based adaptation. This is found to not only be feasible, but preferable, provided there is sufficient facilitation capacity on hand to manage the added complexity that FCM carries over simple narrative scenario development. Adding to the value that FCM offers in adaptation contexts, the thesis also explores its value as both a diagnostic tool for establishing what additional capacity building or data may be required by adaptation decision makers, and also as a tool for gauging the extent to which resilience gains (or losses) might be measured. Although FCM cannot be claimed to provide a robust objective measure of resilience gains or losses, it can nevertheless usefully illustrate to decision makers the strengths and limitations of their own understanding of the systems which they must manage. This is perhaps where the future of FCM-based systems analysis in support of adaptation may ultimately lie