**1. Introduction**

Bio-inspired algorithms nowadays resolve application problems in decisionmaking, information handling, and optimization purposes from different domains of science and engineering. Many techniques developed fields expected to next few years intelligent optimization algorithms more effective in solving different problems for

anomaly and failure detection areas [1]. Optimization plays a major role in more single or multi-objective problems with deterministic or stochastic algorithms [2]. The focus of NP-hard problem-based deterministic or stochastic algorithms to intensification and diversification of meta-heuristic optimization algorithm. Compared to conventional methods, bio-inspired algorithms are intelligent, improved, easy to test, and flexible [3].

In computer networks, security, mechanical problems, electronics image processing, electrical, robotics, production engineering, management, planetary and others are applying bio-inspired algorithms in new era to solve problems easily [4, 5]. Hence it is an emerging field, authors aim to review the discussion and future scope of bio-inspired algorithms. Bio-inspired algorithms concern definitions, principles models, processing steps, merits and demerits reviewed for the most frequently applied bio-inspired algorithms in this chapter. The study discusses bioinspired algorithms which are purely inspired by identifiable or special behaviour of biological organisms. This chapter covers both emerging and well-known techniques. Ten bio-inspired algorithms: Particle swarm optimization (PSO), Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), cuckoo Search Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm are analysed deeply in this work along with their future scope. Authors have restricted to ten potential algorithms few more potential bio-inspired algorithms is dealt in detail for authors other publications [6, 7]. The work carried on in two phases, in initial phase aims in recognizing algorithms and second phase in depth study of identified algorithms is performed. The chapter noticeably aid in identification of significant bio-inspired solutions for various problems. In section 1, overview of optimization technique and types are presented. Section 2 covers core part of authors work which gives in-depth information on ten bio-inspired algorithms. Section 3 focus on current observation of algorithms and in next section further scope and conclusion are briefed.
